idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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55,401 | How to handle both text and numbers for PCA in R? | Make variables
Make your text values nominal variables using factor or ordinal variables using ordered e.g. if you have a data frame called mydata then
mydata <- transform(race=factor(race))
will convert a textual race variable to a nominal one. See ?transform for details.
This way R will show you labels but construc... | How to handle both text and numbers for PCA in R? | Make variables
Make your text values nominal variables using factor or ordinal variables using ordered e.g. if you have a data frame called mydata then
mydata <- transform(race=factor(race))
will con | How to handle both text and numbers for PCA in R?
Make variables
Make your text values nominal variables using factor or ordinal variables using ordered e.g. if you have a data frame called mydata then
mydata <- transform(race=factor(race))
will convert a textual race variable to a nominal one. See ?transform for det... | How to handle both text and numbers for PCA in R?
Make variables
Make your text values nominal variables using factor or ordinal variables using ordered e.g. if you have a data frame called mydata then
mydata <- transform(race=factor(race))
will con |
55,402 | How to check whether maximum likelihood estimation optimizer has converged in R? | If you're using the arima function as
mod <- arima(rates,c(p,d,q))
then the convergence status from the underlying optim routine is
mod$code
If this is 0 then you converged, at least as far as optim was concerned. See ?optim for details.
This is all in the help page: ?arima | How to check whether maximum likelihood estimation optimizer has converged in R? | If you're using the arima function as
mod <- arima(rates,c(p,d,q))
then the convergence status from the underlying optim routine is
mod$code
If this is 0 then you converged, at least as far as opt | How to check whether maximum likelihood estimation optimizer has converged in R?
If you're using the arima function as
mod <- arima(rates,c(p,d,q))
then the convergence status from the underlying optim routine is
mod$code
If this is 0 then you converged, at least as far as optim was concerned. See ?optim for detai... | How to check whether maximum likelihood estimation optimizer has converged in R?
If you're using the arima function as
mod <- arima(rates,c(p,d,q))
then the convergence status from the underlying optim routine is
mod$code
If this is 0 then you converged, at least as far as opt |
55,403 | How to check whether maximum likelihood estimation optimizer has converged in R? | Assuming you have a globally convex likelihood and a regular parameter space, then the optimizer may have not converged when it reaches a boundary of the parameter space. This yeilds parameter estimates which explode and information matrices which are singular. Usually any graphical method is good at diagnosing that. S... | How to check whether maximum likelihood estimation optimizer has converged in R? | Assuming you have a globally convex likelihood and a regular parameter space, then the optimizer may have not converged when it reaches a boundary of the parameter space. This yeilds parameter estimat | How to check whether maximum likelihood estimation optimizer has converged in R?
Assuming you have a globally convex likelihood and a regular parameter space, then the optimizer may have not converged when it reaches a boundary of the parameter space. This yeilds parameter estimates which explode and information matric... | How to check whether maximum likelihood estimation optimizer has converged in R?
Assuming you have a globally convex likelihood and a regular parameter space, then the optimizer may have not converged when it reaches a boundary of the parameter space. This yeilds parameter estimat |
55,404 | Should Kevin Durant be benched? | There has actually been some research on this(!). Here's a paper by Maymin et al. (2012), (SSRN Link). They actually look at win-loss statistics instead of just trying to maximize the number of minutes a player plays. They argue that:
Getting into foul trouble causes a player to play measurably worse, and so
It's rati... | Should Kevin Durant be benched? | There has actually been some research on this(!). Here's a paper by Maymin et al. (2012), (SSRN Link). They actually look at win-loss statistics instead of just trying to maximize the number of minute | Should Kevin Durant be benched?
There has actually been some research on this(!). Here's a paper by Maymin et al. (2012), (SSRN Link). They actually look at win-loss statistics instead of just trying to maximize the number of minutes a player plays. They argue that:
Getting into foul trouble causes a player to play me... | Should Kevin Durant be benched?
There has actually been some research on this(!). Here's a paper by Maymin et al. (2012), (SSRN Link). They actually look at win-loss statistics instead of just trying to maximize the number of minute |
55,405 | Should Kevin Durant be benched? | If we stick rigidly to the assumption "any minute is as likely to draw a foul as the next" then we can model this simply as a Poisson process. The results are shown in the figure below.
The vertical axis shows the probability that Durant has not been fouled out after playing the number of minutes shown on the horizont... | Should Kevin Durant be benched? | If we stick rigidly to the assumption "any minute is as likely to draw a foul as the next" then we can model this simply as a Poisson process. The results are shown in the figure below.
The vertical | Should Kevin Durant be benched?
If we stick rigidly to the assumption "any minute is as likely to draw a foul as the next" then we can model this simply as a Poisson process. The results are shown in the figure below.
The vertical axis shows the probability that Durant has not been fouled out after playing the number ... | Should Kevin Durant be benched?
If we stick rigidly to the assumption "any minute is as likely to draw a foul as the next" then we can model this simply as a Poisson process. The results are shown in the figure below.
The vertical |
55,406 | Performance metric for algorithm predicting probability of low probability events | Mean squared error as suggested by Lakret will certainly work, however, I'd like to propose a method which captures the uncertainty of the clickrates of the adds (which are not known, exactly, but only estimated from historic data).
Let's say we have an add in our validation set with 10000 showns and 10 clicks, i.e. th... | Performance metric for algorithm predicting probability of low probability events | Mean squared error as suggested by Lakret will certainly work, however, I'd like to propose a method which captures the uncertainty of the clickrates of the adds (which are not known, exactly, but onl | Performance metric for algorithm predicting probability of low probability events
Mean squared error as suggested by Lakret will certainly work, however, I'd like to propose a method which captures the uncertainty of the clickrates of the adds (which are not known, exactly, but only estimated from historic data).
Let's... | Performance metric for algorithm predicting probability of low probability events
Mean squared error as suggested by Lakret will certainly work, however, I'd like to propose a method which captures the uncertainty of the clickrates of the adds (which are not known, exactly, but onl |
55,407 | Performance metric for algorithm predicting probability of low probability events | Since your actual data is bivariate (click/no click) you should make your predictions discrete as well (e.g. if predicted probability is more than some value then assign it 1). Then you can employ metrics like precision or accuracy. | Performance metric for algorithm predicting probability of low probability events | Since your actual data is bivariate (click/no click) you should make your predictions discrete as well (e.g. if predicted probability is more than some value then assign it 1). Then you can employ met | Performance metric for algorithm predicting probability of low probability events
Since your actual data is bivariate (click/no click) you should make your predictions discrete as well (e.g. if predicted probability is more than some value then assign it 1). Then you can employ metrics like precision or accuracy. | Performance metric for algorithm predicting probability of low probability events
Since your actual data is bivariate (click/no click) you should make your predictions discrete as well (e.g. if predicted probability is more than some value then assign it 1). Then you can employ met |
55,408 | Performance metric for algorithm predicting probability of low probability events | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
Mean absolute error and Mean squared error may be help... | Performance metric for algorithm predicting probability of low probability events | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| Performance metric for algorithm predicting probability of low probability events
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | Performance metric for algorithm predicting probability of low probability events
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
55,409 | Performance metric for algorithm predicting probability of low probability events | why not simply use the correlation coefficient between the predicted click probability and the click event (0 or 1)? Higher the correlation, better the algorithm. | Performance metric for algorithm predicting probability of low probability events | why not simply use the correlation coefficient between the predicted click probability and the click event (0 or 1)? Higher the correlation, better the algorithm. | Performance metric for algorithm predicting probability of low probability events
why not simply use the correlation coefficient between the predicted click probability and the click event (0 or 1)? Higher the correlation, better the algorithm. | Performance metric for algorithm predicting probability of low probability events
why not simply use the correlation coefficient between the predicted click probability and the click event (0 or 1)? Higher the correlation, better the algorithm. |
55,410 | Performance metric for algorithm predicting probability of low probability events | I found this approach works well:
If there are $N$ examples where we have to calculate CTRs, following is a metric for the performance of algorithm:
Let our algorithm predict a $ctr$ of $ctr(i)$ for example $i$:
$$
v = \sum_{i =1}^{N}\frac{p(i)}{N}
$$
where
$p(i) = ctr(i)$ if actually there was a click
$p(i) = 1 - ctr... | Performance metric for algorithm predicting probability of low probability events | I found this approach works well:
If there are $N$ examples where we have to calculate CTRs, following is a metric for the performance of algorithm:
Let our algorithm predict a $ctr$ of $ctr(i)$ for e | Performance metric for algorithm predicting probability of low probability events
I found this approach works well:
If there are $N$ examples where we have to calculate CTRs, following is a metric for the performance of algorithm:
Let our algorithm predict a $ctr$ of $ctr(i)$ for example $i$:
$$
v = \sum_{i =1}^{N}\fra... | Performance metric for algorithm predicting probability of low probability events
I found this approach works well:
If there are $N$ examples where we have to calculate CTRs, following is a metric for the performance of algorithm:
Let our algorithm predict a $ctr$ of $ctr(i)$ for e |
55,411 | Fitting an exponential mixture model with interval constraints on the mixture weights | Best is not a solution in Excel -- spreadsheets are not a good environment for data analysis: http://www.burns-stat.com/pages/Tutor/spreadsheet_addiction.html
The 'nls' function in R would be one choice. | Fitting an exponential mixture model with interval constraints on the mixture weights | Best is not a solution in Excel -- spreadsheets are not a good environment for data analysis: http://www.burns-stat.com/pages/Tutor/spreadsheet_addiction.html
The 'nls' function in R would be one cho | Fitting an exponential mixture model with interval constraints on the mixture weights
Best is not a solution in Excel -- spreadsheets are not a good environment for data analysis: http://www.burns-stat.com/pages/Tutor/spreadsheet_addiction.html
The 'nls' function in R would be one choice. | Fitting an exponential mixture model with interval constraints on the mixture weights
Best is not a solution in Excel -- spreadsheets are not a good environment for data analysis: http://www.burns-stat.com/pages/Tutor/spreadsheet_addiction.html
The 'nls' function in R would be one cho |
55,412 | Fitting an exponential mixture model with interval constraints on the mixture weights | I'll show you how to analyze your data with Mathematica. First I'll use your model as requested.
data = Import["Desktop/data.csv"];
y = NonlinearModelFit[data,
a Exp[b x] + c Exp[d x] + e, {a, b, c, d, e}, x];
Mathematica returns this error:
NonlinearModelFit::cvmit: Failed to converge to the requested accuracy o... | Fitting an exponential mixture model with interval constraints on the mixture weights | I'll show you how to analyze your data with Mathematica. First I'll use your model as requested.
data = Import["Desktop/data.csv"];
y = NonlinearModelFit[data,
a Exp[b x] + c Exp[d x] + e, {a, b, | Fitting an exponential mixture model with interval constraints on the mixture weights
I'll show you how to analyze your data with Mathematica. First I'll use your model as requested.
data = Import["Desktop/data.csv"];
y = NonlinearModelFit[data,
a Exp[b x] + c Exp[d x] + e, {a, b, c, d, e}, x];
Mathematica returns... | Fitting an exponential mixture model with interval constraints on the mixture weights
I'll show you how to analyze your data with Mathematica. First I'll use your model as requested.
data = Import["Desktop/data.csv"];
y = NonlinearModelFit[data,
a Exp[b x] + c Exp[d x] + e, {a, b, |
55,413 | Fitting an exponential mixture model with interval constraints on the mixture weights | Are you looking for methods or software to implement it? It looks like a typical nonlinear regression problem. In SAS this is implemented using proc nlin. | Fitting an exponential mixture model with interval constraints on the mixture weights | Are you looking for methods or software to implement it? It looks like a typical nonlinear regression problem. In SAS this is implemented using proc nlin. | Fitting an exponential mixture model with interval constraints on the mixture weights
Are you looking for methods or software to implement it? It looks like a typical nonlinear regression problem. In SAS this is implemented using proc nlin. | Fitting an exponential mixture model with interval constraints on the mixture weights
Are you looking for methods or software to implement it? It looks like a typical nonlinear regression problem. In SAS this is implemented using proc nlin. |
55,414 | How to reduce autocorrelation in Metropolis algorithm? | First of all, for your model to be hierarchical, you need hyperpriors for $\alpha$ and $\beta$ (as already explained by Procrastinator). For the sake of simplicity, lets assume uniform priors on the positive part of the real axis. So that have a hierarchical model as follows: $$y_{i}| \lambda_{i}\sim Poisson(\lambda_{... | How to reduce autocorrelation in Metropolis algorithm? | First of all, for your model to be hierarchical, you need hyperpriors for $\alpha$ and $\beta$ (as already explained by Procrastinator). For the sake of simplicity, lets assume uniform priors on the p | How to reduce autocorrelation in Metropolis algorithm?
First of all, for your model to be hierarchical, you need hyperpriors for $\alpha$ and $\beta$ (as already explained by Procrastinator). For the sake of simplicity, lets assume uniform priors on the positive part of the real axis. So that have a hierarchical model ... | How to reduce autocorrelation in Metropolis algorithm?
First of all, for your model to be hierarchical, you need hyperpriors for $\alpha$ and $\beta$ (as already explained by Procrastinator). For the sake of simplicity, lets assume uniform priors on the p |
55,415 | How to reduce autocorrelation in Metropolis algorithm? | Can't seem to figure out how to add a comment, so this is part comment part answer:
First, it sounds from the comments that you are using a Random Walk Metropolis step to update $\alpha$ and $\beta$ jointly (or possibly separately), and then a Gibbs step to update each $\lambda_i$ conditional on the current values for ... | How to reduce autocorrelation in Metropolis algorithm? | Can't seem to figure out how to add a comment, so this is part comment part answer:
First, it sounds from the comments that you are using a Random Walk Metropolis step to update $\alpha$ and $\beta$ j | How to reduce autocorrelation in Metropolis algorithm?
Can't seem to figure out how to add a comment, so this is part comment part answer:
First, it sounds from the comments that you are using a Random Walk Metropolis step to update $\alpha$ and $\beta$ jointly (or possibly separately), and then a Gibbs step to update ... | How to reduce autocorrelation in Metropolis algorithm?
Can't seem to figure out how to add a comment, so this is part comment part answer:
First, it sounds from the comments that you are using a Random Walk Metropolis step to update $\alpha$ and $\beta$ j |
55,416 | SVM using RBF and nearest neighbor classification method | The decision boundary for 1-NN algorithm is the union of the Voronoi cells of each training instance.
As for SVM, when you use RBf kernel and if there is no regularization, the decision boundary will also be an approximation of the union of the Voronoi cells. So in this case, these methods are the same in terms of per... | SVM using RBF and nearest neighbor classification method | The decision boundary for 1-NN algorithm is the union of the Voronoi cells of each training instance.
As for SVM, when you use RBf kernel and if there is no regularization, the decision boundary will | SVM using RBF and nearest neighbor classification method
The decision boundary for 1-NN algorithm is the union of the Voronoi cells of each training instance.
As for SVM, when you use RBf kernel and if there is no regularization, the decision boundary will also be an approximation of the union of the Voronoi cells. So... | SVM using RBF and nearest neighbor classification method
The decision boundary for 1-NN algorithm is the union of the Voronoi cells of each training instance.
As for SVM, when you use RBf kernel and if there is no regularization, the decision boundary will |
55,417 | SVM using RBF and nearest neighbor classification method | They are not that similar, but they are related though. The point is, that both kNN and RBF are non-parametric methods to estimate the density of probability of your data.
To see this let us first consider the case of kernel methods. Say you consider a region of the feature space $R$. If you draw sample points from the... | SVM using RBF and nearest neighbor classification method | They are not that similar, but they are related though. The point is, that both kNN and RBF are non-parametric methods to estimate the density of probability of your data.
To see this let us first con | SVM using RBF and nearest neighbor classification method
They are not that similar, but they are related though. The point is, that both kNN and RBF are non-parametric methods to estimate the density of probability of your data.
To see this let us first consider the case of kernel methods. Say you consider a region of ... | SVM using RBF and nearest neighbor classification method
They are not that similar, but they are related though. The point is, that both kNN and RBF are non-parametric methods to estimate the density of probability of your data.
To see this let us first con |
55,418 | Definition of "Natural Images" in the context of machine learning | I suspect it depends on the context.
In the example you gave, they are contrasting handwritten characters with photographs. Both obviously have covariance structure. However, there are a finite number of letters (26, 52, etc) and the covariance structure of a letter is, by convention, pretty tightly constrained. The nu... | Definition of "Natural Images" in the context of machine learning | I suspect it depends on the context.
In the example you gave, they are contrasting handwritten characters with photographs. Both obviously have covariance structure. However, there are a finite number | Definition of "Natural Images" in the context of machine learning
I suspect it depends on the context.
In the example you gave, they are contrasting handwritten characters with photographs. Both obviously have covariance structure. However, there are a finite number of letters (26, 52, etc) and the covariance structure... | Definition of "Natural Images" in the context of machine learning
I suspect it depends on the context.
In the example you gave, they are contrasting handwritten characters with photographs. Both obviously have covariance structure. However, there are a finite number |
55,419 | What model should I use for this research? | An example
If you're happy with R you might want to look at the Seatbelts dataset that's built into the base R distribution. It sounds very similar. Like your problem, this consists of several time series of counts (front seat casualties) and an intervention/treatment (imposition of a seatbelt law) that affects only ... | What model should I use for this research? | An example
If you're happy with R you might want to look at the Seatbelts dataset that's built into the base R distribution. It sounds very similar. Like your problem, this consists of several time | What model should I use for this research?
An example
If you're happy with R you might want to look at the Seatbelts dataset that's built into the base R distribution. It sounds very similar. Like your problem, this consists of several time series of counts (front seat casualties) and an intervention/treatment (impos... | What model should I use for this research?
An example
If you're happy with R you might want to look at the Seatbelts dataset that's built into the base R distribution. It sounds very similar. Like your problem, this consists of several time |
55,420 | What model should I use for this research? | First, I would try to put both series on a comparable scale. For example, you might look at the revenue divided by population of movie-going age over time. I would more confident if the two series looked very similar before the event, but then diverged after.
Another approach might be the difference in differences est... | What model should I use for this research? | First, I would try to put both series on a comparable scale. For example, you might look at the revenue divided by population of movie-going age over time. I would more confident if the two series loo | What model should I use for this research?
First, I would try to put both series on a comparable scale. For example, you might look at the revenue divided by population of movie-going age over time. I would more confident if the two series looked very similar before the event, but then diverged after.
Another approach... | What model should I use for this research?
First, I would try to put both series on a comparable scale. For example, you might look at the revenue divided by population of movie-going age over time. I would more confident if the two series loo |
55,421 | What model should I use for this research? | To explore the data you could use descriptives and it's very important to plot the data to get a visual representation and a feel for the data.
It sounds like a regression could work for you, using dummies for the events and the different countries. Also, you could decide to give the countries a different slope and in... | What model should I use for this research? | To explore the data you could use descriptives and it's very important to plot the data to get a visual representation and a feel for the data.
It sounds like a regression could work for you, using d | What model should I use for this research?
To explore the data you could use descriptives and it's very important to plot the data to get a visual representation and a feel for the data.
It sounds like a regression could work for you, using dummies for the events and the different countries. Also, you could decide to ... | What model should I use for this research?
To explore the data you could use descriptives and it's very important to plot the data to get a visual representation and a feel for the data.
It sounds like a regression could work for you, using d |
55,422 | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other? | I think the Mann-Whitney/Wilcoxon ranked-sum test is the appropriate test. The KS test is specifically for comparing continuous distributions - your ratings are ordinal, so it doesn't seem appropriate here.
The t-test and the Wilcoxon ranked-sum differ in that the t-test is comparing the means of the two distributions... | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other? | I think the Mann-Whitney/Wilcoxon ranked-sum test is the appropriate test. The KS test is specifically for comparing continuous distributions - your ratings are ordinal, so it doesn't seem appropriat | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other?
I think the Mann-Whitney/Wilcoxon ranked-sum test is the appropriate test. The KS test is specifically for comparing continuous distributions - your ratings are ordinal, so it doesn't seem appropriate here.
The t-test and the... | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other?
I think the Mann-Whitney/Wilcoxon ranked-sum test is the appropriate test. The KS test is specifically for comparing continuous distributions - your ratings are ordinal, so it doesn't seem appropriat |
55,423 | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other? | Plainly there's a difference between the groups - the locations are completely different, with no overlap in the groups. Any sensible test will reject even at this small sample size.
I assume you want to ask a different question of the data than that. | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other? | Plainly there's a difference between the groups - the locations are completely different, with no overlap in the groups. Any sensible test will reject even at this small sample size.
I assume you want | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other?
Plainly there's a difference between the groups - the locations are completely different, with no overlap in the groups. Any sensible test will reject even at this small sample size.
I assume you want to ask a different questi... | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other?
Plainly there's a difference between the groups - the locations are completely different, with no overlap in the groups. Any sensible test will reject even at this small sample size.
I assume you want |
55,424 | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other? | You should t-test the mean difference between smart phone users and non-smart phone users. Pearson's R is not appropriate for binary variables because it assumes that both variables are normally distributed (and X, in your case, cannot be because it is dichotomous).
A t-test will ask if the mean score on the outcome (... | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other? | You should t-test the mean difference between smart phone users and non-smart phone users. Pearson's R is not appropriate for binary variables because it assumes that both variables are normally dist | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other?
You should t-test the mean difference between smart phone users and non-smart phone users. Pearson's R is not appropriate for binary variables because it assumes that both variables are normally distributed (and X, in your ca... | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other?
You should t-test the mean difference between smart phone users and non-smart phone users. Pearson's R is not appropriate for binary variables because it assumes that both variables are normally dist |
55,425 | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other? | Another option is Pearson's Chi-square test, which is appropriate for categorical variables. You can use it to test whether the variable Y is independent of X, in other words, whether Y has any influence on X. However, you would need a sample larger than 10 values, and also I'm not sure if this test can be used with a ... | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other? | Another option is Pearson's Chi-square test, which is appropriate for categorical variables. You can use it to test whether the variable Y is independent of X, in other words, whether Y has any influe | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other?
Another option is Pearson's Chi-square test, which is appropriate for categorical variables. You can use it to test whether the variable Y is independent of X, in other words, whether Y has any influence on X. However, you wou... | Choosing the right test when comparing two groups: Mann-Whitney, Kolmogorov-Smirnov or other?
Another option is Pearson's Chi-square test, which is appropriate for categorical variables. You can use it to test whether the variable Y is independent of X, in other words, whether Y has any influe |
55,426 | Panel data model estimation with dummy variables | A possible reason might be that your dummies do not vary over time. In this case, the fixed effects estimator and first differencing will remove such variables from the model. The reason is that these estimators cannot identify variables that do not vary over time because those variables will be eliminated together wit... | Panel data model estimation with dummy variables | A possible reason might be that your dummies do not vary over time. In this case, the fixed effects estimator and first differencing will remove such variables from the model. The reason is that these | Panel data model estimation with dummy variables
A possible reason might be that your dummies do not vary over time. In this case, the fixed effects estimator and first differencing will remove such variables from the model. The reason is that these estimators cannot identify variables that do not vary over time becaus... | Panel data model estimation with dummy variables
A possible reason might be that your dummies do not vary over time. In this case, the fixed effects estimator and first differencing will remove such variables from the model. The reason is that these |
55,427 | Panel data model estimation with dummy variables | Another possibility is that the dummy variables are (very close to) co-linear. In this case, plm will automatically exclude the co-linear variables from its output. One way to check this is to run the model, and check the aliased object:
model.FE<- plm(income~area+weight+dproduct+dummy1+dummy2+dummy3,data=panel, mode... | Panel data model estimation with dummy variables | Another possibility is that the dummy variables are (very close to) co-linear. In this case, plm will automatically exclude the co-linear variables from its output. One way to check this is to run the | Panel data model estimation with dummy variables
Another possibility is that the dummy variables are (very close to) co-linear. In this case, plm will automatically exclude the co-linear variables from its output. One way to check this is to run the model, and check the aliased object:
model.FE<- plm(income~area+weight... | Panel data model estimation with dummy variables
Another possibility is that the dummy variables are (very close to) co-linear. In this case, plm will automatically exclude the co-linear variables from its output. One way to check this is to run the |
55,428 | Problem in discrete valued time series forecasting | Returning to the discarded HMM approach. An HMM is just a state space model that assumes discrete hidden state. I think you have dismissed the state space approach too soon.
The forecasting question you bring up is quite general for such models which I'll discuss in discrete time formulation, since that's what you've... | Problem in discrete valued time series forecasting | Returning to the discarded HMM approach. An HMM is just a state space model that assumes discrete hidden state. I think you have dismissed the state space approach too soon.
The forecasting question | Problem in discrete valued time series forecasting
Returning to the discarded HMM approach. An HMM is just a state space model that assumes discrete hidden state. I think you have dismissed the state space approach too soon.
The forecasting question you bring up is quite general for such models which I'll discuss in ... | Problem in discrete valued time series forecasting
Returning to the discarded HMM approach. An HMM is just a state space model that assumes discrete hidden state. I think you have dismissed the state space approach too soon.
The forecasting question |
55,429 | Problem in discrete valued time series forecasting | The airline series is a count series i.e the number of people flying per month. You could consider your series to be also a count series ( in reality it is an attribute/class series) but at a much lower level (1,2,3,4,5,6). Now if you were to identify and estimate an ARIMA model the problem arises insofar as the fitted... | Problem in discrete valued time series forecasting | The airline series is a count series i.e the number of people flying per month. You could consider your series to be also a count series ( in reality it is an attribute/class series) but at a much low | Problem in discrete valued time series forecasting
The airline series is a count series i.e the number of people flying per month. You could consider your series to be also a count series ( in reality it is an attribute/class series) but at a much lower level (1,2,3,4,5,6). Now if you were to identify and estimate an A... | Problem in discrete valued time series forecasting
The airline series is a count series i.e the number of people flying per month. You could consider your series to be also a count series ( in reality it is an attribute/class series) but at a much low |
55,430 | How to retrieve model-specific information when using the caret package? | The train function in caret returns an S3 list object. On of the items in this list is called finalModel. This item is just the fitted object in it's native form. So if you trained a gbm using train and stored it in an object called my.fit, then summary(my.fit$finalModel) would use the summary.gbm method from the gb... | How to retrieve model-specific information when using the caret package? | The train function in caret returns an S3 list object. On of the items in this list is called finalModel. This item is just the fitted object in it's native form. So if you trained a gbm using trai | How to retrieve model-specific information when using the caret package?
The train function in caret returns an S3 list object. On of the items in this list is called finalModel. This item is just the fitted object in it's native form. So if you trained a gbm using train and stored it in an object called my.fit, the... | How to retrieve model-specific information when using the caret package?
The train function in caret returns an S3 list object. On of the items in this list is called finalModel. This item is just the fitted object in it's native form. So if you trained a gbm using trai |
55,431 | How can I discern whether a classifier's outcome is significantly different? | It's hard to derive anything of meaning from what is essentially 5 data points.
I would use a Hamming distance from a reference vector and accept any results that are less than n away from that.
It's not perfect, and it's probably not publishable, but you can get an idea for your own benchmarking and perhaps cross-... | How can I discern whether a classifier's outcome is significantly different? | It's hard to derive anything of meaning from what is essentially 5 data points.
I would use a Hamming distance from a reference vector and accept any results that are less than n away from that.
I | How can I discern whether a classifier's outcome is significantly different?
It's hard to derive anything of meaning from what is essentially 5 data points.
I would use a Hamming distance from a reference vector and accept any results that are less than n away from that.
It's not perfect, and it's probably not publ... | How can I discern whether a classifier's outcome is significantly different?
It's hard to derive anything of meaning from what is essentially 5 data points.
I would use a Hamming distance from a reference vector and accept any results that are less than n away from that.
I |
55,432 | How can I discern whether a classifier's outcome is significantly different? | Firstly I'd agree with @jonsca - there's now way to derive anything from so few data points.
When you posted on stats.SE, you made a comment that:
I would like to do a statistic test to test whether those results are different from each other ( which I don't know exactly how to do, this is why I'm asking here....). fr... | How can I discern whether a classifier's outcome is significantly different? | Firstly I'd agree with @jonsca - there's now way to derive anything from so few data points.
When you posted on stats.SE, you made a comment that:
I would like to do a statistic test to test whether | How can I discern whether a classifier's outcome is significantly different?
Firstly I'd agree with @jonsca - there's now way to derive anything from so few data points.
When you posted on stats.SE, you made a comment that:
I would like to do a statistic test to test whether those results are different from each other... | How can I discern whether a classifier's outcome is significantly different?
Firstly I'd agree with @jonsca - there's now way to derive anything from so few data points.
When you posted on stats.SE, you made a comment that:
I would like to do a statistic test to test whether |
55,433 | Convergence in distribution, probability, and 2nd mean | To prove that $X_n \stackrel{P}{\rightarrow} X$ you have to show that, for any given $\epsilon>0$, the sequence of real numbers $\{p_n\}_{n\geq 1}$ defined by
$$
p_n = P\{\omega:|X_n(\omega)-X(\omega)|\geq \epsilon\}
$$
has limit $0$. What happens at the ``beginning'' of the sequence does not change its limit. For $n... | Convergence in distribution, probability, and 2nd mean | To prove that $X_n \stackrel{P}{\rightarrow} X$ you have to show that, for any given $\epsilon>0$, the sequence of real numbers $\{p_n\}_{n\geq 1}$ defined by
$$
p_n = P\{\omega:|X_n(\omega)-X(\omeg | Convergence in distribution, probability, and 2nd mean
To prove that $X_n \stackrel{P}{\rightarrow} X$ you have to show that, for any given $\epsilon>0$, the sequence of real numbers $\{p_n\}_{n\geq 1}$ defined by
$$
p_n = P\{\omega:|X_n(\omega)-X(\omega)|\geq \epsilon\}
$$
has limit $0$. What happens at the ``beginn... | Convergence in distribution, probability, and 2nd mean
To prove that $X_n \stackrel{P}{\rightarrow} X$ you have to show that, for any given $\epsilon>0$, the sequence of real numbers $\{p_n\}_{n\geq 1}$ defined by
$$
p_n = P\{\omega:|X_n(\omega)-X(\omeg |
55,434 | Create positive-definite 3x3 covariance matrix given specified correlation values | To follow up on @cardinal's comment: your $x$, $y$, and $z$ define a $(3 \times 3)$ correlation matrix $R$. Since a correlation matrix also is a possible covariance matrix (of standardized variables), it has to be positive definite. This is the case if all eigenvalues are $> 0$. If $R$ is indeed positive definite, then... | Create positive-definite 3x3 covariance matrix given specified correlation values | To follow up on @cardinal's comment: your $x$, $y$, and $z$ define a $(3 \times 3)$ correlation matrix $R$. Since a correlation matrix also is a possible covariance matrix (of standardized variables), | Create positive-definite 3x3 covariance matrix given specified correlation values
To follow up on @cardinal's comment: your $x$, $y$, and $z$ define a $(3 \times 3)$ correlation matrix $R$. Since a correlation matrix also is a possible covariance matrix (of standardized variables), it has to be positive definite. This ... | Create positive-definite 3x3 covariance matrix given specified correlation values
To follow up on @cardinal's comment: your $x$, $y$, and $z$ define a $(3 \times 3)$ correlation matrix $R$. Since a correlation matrix also is a possible covariance matrix (of standardized variables), |
55,435 | Create positive-definite 3x3 covariance matrix given specified correlation values | If you're not given a complete collection of correlations, but only a partial set, then you can construct a semidefinite programming feasibility problem that can be used to determine whether or not there is a correlation matrix that has the specified correlations. This can be solved by various SDP solvers such as SeDu... | Create positive-definite 3x3 covariance matrix given specified correlation values | If you're not given a complete collection of correlations, but only a partial set, then you can construct a semidefinite programming feasibility problem that can be used to determine whether or not th | Create positive-definite 3x3 covariance matrix given specified correlation values
If you're not given a complete collection of correlations, but only a partial set, then you can construct a semidefinite programming feasibility problem that can be used to determine whether or not there is a correlation matrix that has t... | Create positive-definite 3x3 covariance matrix given specified correlation values
If you're not given a complete collection of correlations, but only a partial set, then you can construct a semidefinite programming feasibility problem that can be used to determine whether or not th |
55,436 | How to avoid and handle survey non-response? | In some cases it is possible to make adjustments and "correct" survey non-response. The topic is i.a. discussed in this post. However, your situation seems to be a bit special. You have a list of users, but no information about their profiles.
The best thing to do about non-response, be it in your particular case or i... | How to avoid and handle survey non-response? | In some cases it is possible to make adjustments and "correct" survey non-response. The topic is i.a. discussed in this post. However, your situation seems to be a bit special. You have a list of user | How to avoid and handle survey non-response?
In some cases it is possible to make adjustments and "correct" survey non-response. The topic is i.a. discussed in this post. However, your situation seems to be a bit special. You have a list of users, but no information about their profiles.
The best thing to do about non... | How to avoid and handle survey non-response?
In some cases it is possible to make adjustments and "correct" survey non-response. The topic is i.a. discussed in this post. However, your situation seems to be a bit special. You have a list of user |
55,437 | How to avoid and handle survey non-response? | Make it optional to answer the gender question. That way, it's more likely to be accurate.
You will never know for sure the exact number of males and females because there will always be some sort of sampling error in your data -- you can't avoid that unless you interviewed every single customer.
Selecting how many sam... | How to avoid and handle survey non-response? | Make it optional to answer the gender question. That way, it's more likely to be accurate.
You will never know for sure the exact number of males and females because there will always be some sort of | How to avoid and handle survey non-response?
Make it optional to answer the gender question. That way, it's more likely to be accurate.
You will never know for sure the exact number of males and females because there will always be some sort of sampling error in your data -- you can't avoid that unless you interviewed ... | How to avoid and handle survey non-response?
Make it optional to answer the gender question. That way, it's more likely to be accurate.
You will never know for sure the exact number of males and females because there will always be some sort of |
55,438 | How to avoid and handle survey non-response? | You called them customers. What is the nature of the relationship? Is it possible to gather the information for billing purposes? Of course if you are dealing with businesses the contact/billing person is the choice of the business, they may not be the decision maker. | How to avoid and handle survey non-response? | You called them customers. What is the nature of the relationship? Is it possible to gather the information for billing purposes? Of course if you are dealing with businesses the contact/billing perso | How to avoid and handle survey non-response?
You called them customers. What is the nature of the relationship? Is it possible to gather the information for billing purposes? Of course if you are dealing with businesses the contact/billing person is the choice of the business, they may not be the decision maker. | How to avoid and handle survey non-response?
You called them customers. What is the nature of the relationship? Is it possible to gather the information for billing purposes? Of course if you are dealing with businesses the contact/billing perso |
55,439 | How to avoid and handle survey non-response? | This may be obvious, but I didn't notice it mentioned. I'd look for gender differences on every item on the survey--through comparisons of means, correlations, crosstabs with chi-square tests, even perhaps with logistic regression. If you find no substantial differences, great: gender doesn't matter to representativ... | How to avoid and handle survey non-response? | This may be obvious, but I didn't notice it mentioned. I'd look for gender differences on every item on the survey--through comparisons of means, correlations, crosstabs with chi-square tests, even p | How to avoid and handle survey non-response?
This may be obvious, but I didn't notice it mentioned. I'd look for gender differences on every item on the survey--through comparisons of means, correlations, crosstabs with chi-square tests, even perhaps with logistic regression. If you find no substantial differences, g... | How to avoid and handle survey non-response?
This may be obvious, but I didn't notice it mentioned. I'd look for gender differences on every item on the survey--through comparisons of means, correlations, crosstabs with chi-square tests, even p |
55,440 | P values seem to be the wrong way around | The p value is the probability of making a certain observation - or more extreme - due to chance, given that the observation is drawn from the original distribution.
Normally (haha) you would expect observations to be pretty close to the distribution's arithmetic mean. If an observation is far from the mean it can be ... | P values seem to be the wrong way around | The p value is the probability of making a certain observation - or more extreme - due to chance, given that the observation is drawn from the original distribution.
Normally (haha) you would expect | P values seem to be the wrong way around
The p value is the probability of making a certain observation - or more extreme - due to chance, given that the observation is drawn from the original distribution.
Normally (haha) you would expect observations to be pretty close to the distribution's arithmetic mean. If an ob... | P values seem to be the wrong way around
The p value is the probability of making a certain observation - or more extreme - due to chance, given that the observation is drawn from the original distribution.
Normally (haha) you would expect |
55,441 | Including day of week in a logit model | The model with day as a categorical variable with seven levels does account for each single day; you don't need to do it "by hand", so to speak.
For example:
library(MASS)
# Construct sample data: 700 observations, 100 on each of 7 days of week
Day <- factor(rep(c("Monday","Tuesday","Wednesday","Thursday","Friday","Sa... | Including day of week in a logit model | The model with day as a categorical variable with seven levels does account for each single day; you don't need to do it "by hand", so to speak.
For example:
library(MASS)
# Construct sample data: 70 | Including day of week in a logit model
The model with day as a categorical variable with seven levels does account for each single day; you don't need to do it "by hand", so to speak.
For example:
library(MASS)
# Construct sample data: 700 observations, 100 on each of 7 days of week
Day <- factor(rep(c("Monday","Tuesd... | Including day of week in a logit model
The model with day as a categorical variable with seven levels does account for each single day; you don't need to do it "by hand", so to speak.
For example:
library(MASS)
# Construct sample data: 70 |
55,442 | What kind of cut-offs are used for publication bias tests in meta-analysis? | The use of any fail-safe N method is depreciated. Please see Becker (2005) and check out the Conclusions section. Here, she states:
"Given the other approaches that now exist for dealing with publication bias, the fail-safe N should be abandoned in favour of other, more informative analyses [...]".
You also might w... | What kind of cut-offs are used for publication bias tests in meta-analysis? | The use of any fail-safe N method is depreciated. Please see Becker (2005) and check out the Conclusions section. Here, she states:
"Given the other approaches that now exist for dealing with public | What kind of cut-offs are used for publication bias tests in meta-analysis?
The use of any fail-safe N method is depreciated. Please see Becker (2005) and check out the Conclusions section. Here, she states:
"Given the other approaches that now exist for dealing with publication bias, the fail-safe N should be abando... | What kind of cut-offs are used for publication bias tests in meta-analysis?
The use of any fail-safe N method is depreciated. Please see Becker (2005) and check out the Conclusions section. Here, she states:
"Given the other approaches that now exist for dealing with public |
55,443 | Can structural equation models be used to derive clinical formulae? | If you can build a regression model for something, it means to me that this is a measurable quantity. While a linear regression model is a special case of the general SEM, the greater strength of SEM is, arguably, being able to accommodate latent variables and measurement error in predictors. If you want to build a str... | Can structural equation models be used to derive clinical formulae? | If you can build a regression model for something, it means to me that this is a measurable quantity. While a linear regression model is a special case of the general SEM, the greater strength of SEM | Can structural equation models be used to derive clinical formulae?
If you can build a regression model for something, it means to me that this is a measurable quantity. While a linear regression model is a special case of the general SEM, the greater strength of SEM is, arguably, being able to accommodate latent varia... | Can structural equation models be used to derive clinical formulae?
If you can build a regression model for something, it means to me that this is a measurable quantity. While a linear regression model is a special case of the general SEM, the greater strength of SEM |
55,444 | Can structural equation models be used to derive clinical formulae? | Yes SEM can do that. You enter the measured and latent variables into the model, specify their relationships, and then you will get quite a lot of output. This output will include a structural equation (looks like a regression equation, with coefficients, standard errors, etc) and an R^2 result. SEM also allows you to ... | Can structural equation models be used to derive clinical formulae? | Yes SEM can do that. You enter the measured and latent variables into the model, specify their relationships, and then you will get quite a lot of output. This output will include a structural equatio | Can structural equation models be used to derive clinical formulae?
Yes SEM can do that. You enter the measured and latent variables into the model, specify their relationships, and then you will get quite a lot of output. This output will include a structural equation (looks like a regression equation, with coefficien... | Can structural equation models be used to derive clinical formulae?
Yes SEM can do that. You enter the measured and latent variables into the model, specify their relationships, and then you will get quite a lot of output. This output will include a structural equatio |
55,445 | Handling outliers when comparing two means in a repeated measures design | I will take these out of order. If it is possible to establish a correspondence between the measurements in the first set and the measurements in the second set (for example, Bob's score at time 1 and Bob's score at time 2 correspond because they both came from Bob), then you should do a paired t-test. That is, you s... | Handling outliers when comparing two means in a repeated measures design | I will take these out of order. If it is possible to establish a correspondence between the measurements in the first set and the measurements in the second set (for example, Bob's score at time 1 an | Handling outliers when comparing two means in a repeated measures design
I will take these out of order. If it is possible to establish a correspondence between the measurements in the first set and the measurements in the second set (for example, Bob's score at time 1 and Bob's score at time 2 correspond because they... | Handling outliers when comparing two means in a repeated measures design
I will take these out of order. If it is possible to establish a correspondence between the measurements in the first set and the measurements in the second set (for example, Bob's score at time 1 an |
55,446 | What is a good academic citation for cross-validation? | I find chapter 7 of Hastie, Tibshirani, Friedman's Elements of Statistical Learning to be a good reference for CV and how and why it is used. | What is a good academic citation for cross-validation? | I find chapter 7 of Hastie, Tibshirani, Friedman's Elements of Statistical Learning to be a good reference for CV and how and why it is used. | What is a good academic citation for cross-validation?
I find chapter 7 of Hastie, Tibshirani, Friedman's Elements of Statistical Learning to be a good reference for CV and how and why it is used. | What is a good academic citation for cross-validation?
I find chapter 7 of Hastie, Tibshirani, Friedman's Elements of Statistical Learning to be a good reference for CV and how and why it is used. |
55,447 | Obtaining random number from a mixture of two normal distributions | If you want to sample unequally (with probability 0.7 and 0.3) from two gaussians with parameters $(\mu_1,\sigma_1^2)$ and $(\mu_2,\sigma_2^2)$, then you can probably try something like that:
n <- 100
yn <- rbinom(n, 1, .7)
# draw n units from a mixture of N(0,1) and N(100,3^2)
s <- rnorm(n, 0 + 100*yn, 1 + 2*yn)
In f... | Obtaining random number from a mixture of two normal distributions | If you want to sample unequally (with probability 0.7 and 0.3) from two gaussians with parameters $(\mu_1,\sigma_1^2)$ and $(\mu_2,\sigma_2^2)$, then you can probably try something like that:
n <- 100 | Obtaining random number from a mixture of two normal distributions
If you want to sample unequally (with probability 0.7 and 0.3) from two gaussians with parameters $(\mu_1,\sigma_1^2)$ and $(\mu_2,\sigma_2^2)$, then you can probably try something like that:
n <- 100
yn <- rbinom(n, 1, .7)
# draw n units from a mixture... | Obtaining random number from a mixture of two normal distributions
If you want to sample unequally (with probability 0.7 and 0.3) from two gaussians with parameters $(\mu_1,\sigma_1^2)$ and $(\mu_2,\sigma_2^2)$, then you can probably try something like that:
n <- 100 |
55,448 | Obtaining random number from a mixture of two normal distributions | To accomplish the goal of sampling from an uneven mixture of distributions, the most straightforward approach is to sample separately, in proportion to the desired ratio:
p <- 0.70 #P(from N(mu1, sd1))
n.samps <- 10000
mu1 <- 0
sd1 <- 1
mu2 <- 100
sd2 <- 10
x <- vector()
for(i in 1:n.samps){
b <- runif(1,... | Obtaining random number from a mixture of two normal distributions | To accomplish the goal of sampling from an uneven mixture of distributions, the most straightforward approach is to sample separately, in proportion to the desired ratio:
p <- 0.70 #P(from N(mu1, sd1 | Obtaining random number from a mixture of two normal distributions
To accomplish the goal of sampling from an uneven mixture of distributions, the most straightforward approach is to sample separately, in proportion to the desired ratio:
p <- 0.70 #P(from N(mu1, sd1))
n.samps <- 10000
mu1 <- 0
sd1 <- 1
mu2 <- 100... | Obtaining random number from a mixture of two normal distributions
To accomplish the goal of sampling from an uneven mixture of distributions, the most straightforward approach is to sample separately, in proportion to the desired ratio:
p <- 0.70 #P(from N(mu1, sd1 |
55,449 | Issues with the Huynh-Feldt values using either Anova or ezANOVA in R? | If there were an error in Anova it would also be in ezANOVA because Mike Lawrence built ez on John Fox's Anova package. And the Anova package in turn uses the standard R anova command. R is getting this correct. Peter Dalgaard (pp.3-4) pointed out that SAS and SPSS get the H-F calculation wrong, and have for decades... | Issues with the Huynh-Feldt values using either Anova or ezANOVA in R? | If there were an error in Anova it would also be in ezANOVA because Mike Lawrence built ez on John Fox's Anova package. And the Anova package in turn uses the standard R anova command. R is getting | Issues with the Huynh-Feldt values using either Anova or ezANOVA in R?
If there were an error in Anova it would also be in ezANOVA because Mike Lawrence built ez on John Fox's Anova package. And the Anova package in turn uses the standard R anova command. R is getting this correct. Peter Dalgaard (pp.3-4) pointed ou... | Issues with the Huynh-Feldt values using either Anova or ezANOVA in R?
If there were an error in Anova it would also be in ezANOVA because Mike Lawrence built ez on John Fox's Anova package. And the Anova package in turn uses the standard R anova command. R is getting |
55,450 | Expectation of length of a confidence interval | I'm aware that this is a 'homework' question but it has gone unanswered for over six months so I figure the homework has been turned in by now. Also, the hints in the comments (which I use directly here) are useful until you get to the point where you have to calculate the expected value of the sample standard deviatio... | Expectation of length of a confidence interval | I'm aware that this is a 'homework' question but it has gone unanswered for over six months so I figure the homework has been turned in by now. Also, the hints in the comments (which I use directly he | Expectation of length of a confidence interval
I'm aware that this is a 'homework' question but it has gone unanswered for over six months so I figure the homework has been turned in by now. Also, the hints in the comments (which I use directly here) are useful until you get to the point where you have to calculate the... | Expectation of length of a confidence interval
I'm aware that this is a 'homework' question but it has gone unanswered for over six months so I figure the homework has been turned in by now. Also, the hints in the comments (which I use directly he |
55,451 | How to calculate quartiles with only standard deviation and mean assuming normal distribution? | The quantile or probit function, as you can see from the link (see "Computatuon"), is computed with inverse gaussian error function $erf^{-1}$ which I hope is downloadable for calculators like TI-89. Look here for instance. | How to calculate quartiles with only standard deviation and mean assuming normal distribution? | The quantile or probit function, as you can see from the link (see "Computatuon"), is computed with inverse gaussian error function $erf^{-1}$ which I hope is downloadable for calculators like TI-89. | How to calculate quartiles with only standard deviation and mean assuming normal distribution?
The quantile or probit function, as you can see from the link (see "Computatuon"), is computed with inverse gaussian error function $erf^{-1}$ which I hope is downloadable for calculators like TI-89. Look here for instance. | How to calculate quartiles with only standard deviation and mean assuming normal distribution?
The quantile or probit function, as you can see from the link (see "Computatuon"), is computed with inverse gaussian error function $erf^{-1}$ which I hope is downloadable for calculators like TI-89. |
55,452 | How to calculate quartiles with only standard deviation and mean assuming normal distribution? | 2nd Vars (Distr)>"InvNorm" next you subtract 1-% and enter this into your Inverse Norm along with your Mean and standard deviation.
Ex: Find the third quartile Q3 which is the IQ score separating the top 25% from the others. With a Mean of 100 and a Standard Deviation of 15.
1-.25=.75 in Inv Norm (.75,100,15)=110 My... | How to calculate quartiles with only standard deviation and mean assuming normal distribution? | 2nd Vars (Distr)>"InvNorm" next you subtract 1-% and enter this into your Inverse Norm along with your Mean and standard deviation.
Ex: Find the third quartile Q3 which is the IQ score separating the | How to calculate quartiles with only standard deviation and mean assuming normal distribution?
2nd Vars (Distr)>"InvNorm" next you subtract 1-% and enter this into your Inverse Norm along with your Mean and standard deviation.
Ex: Find the third quartile Q3 which is the IQ score separating the top 25% from the others.... | How to calculate quartiles with only standard deviation and mean assuming normal distribution?
2nd Vars (Distr)>"InvNorm" next you subtract 1-% and enter this into your Inverse Norm along with your Mean and standard deviation.
Ex: Find the third quartile Q3 which is the IQ score separating the |
55,453 | How to calculate quartiles with only standard deviation and mean assuming normal distribution? | Finding Q1 and Q3 for a bell curve my textbook says the formula for Q1 is M-(.675)SD=Q1 for Q3 it's M+(.675)SD=Q3. So M is the median and SD is standard deviation and Q1 is minus and Q3 is add. Normal distribution is different than data sets and frequency charts | How to calculate quartiles with only standard deviation and mean assuming normal distribution? | Finding Q1 and Q3 for a bell curve my textbook says the formula for Q1 is M-(.675)SD=Q1 for Q3 it's M+(.675)SD=Q3. So M is the median and SD is standard deviation and Q1 is minus and Q3 is add. Normal | How to calculate quartiles with only standard deviation and mean assuming normal distribution?
Finding Q1 and Q3 for a bell curve my textbook says the formula for Q1 is M-(.675)SD=Q1 for Q3 it's M+(.675)SD=Q3. So M is the median and SD is standard deviation and Q1 is minus and Q3 is add. Normal distribution is differen... | How to calculate quartiles with only standard deviation and mean assuming normal distribution?
Finding Q1 and Q3 for a bell curve my textbook says the formula for Q1 is M-(.675)SD=Q1 for Q3 it's M+(.675)SD=Q3. So M is the median and SD is standard deviation and Q1 is minus and Q3 is add. Normal |
55,454 | Plotting scatterplot with non-dominated front in R | Presumably "runtime" is better when it is low and "solution quality" is better when it is high. To make these variables more consistent in how they represent the attributes, first re-express them so that high numbers always correspond to better values. For this question, the reciprocal of runtime (which could be inte... | Plotting scatterplot with non-dominated front in R | Presumably "runtime" is better when it is low and "solution quality" is better when it is high. To make these variables more consistent in how they represent the attributes, first re-express them so | Plotting scatterplot with non-dominated front in R
Presumably "runtime" is better when it is low and "solution quality" is better when it is high. To make these variables more consistent in how they represent the attributes, first re-express them so that high numbers always correspond to better values. For this quest... | Plotting scatterplot with non-dominated front in R
Presumably "runtime" is better when it is low and "solution quality" is better when it is high. To make these variables more consistent in how they represent the attributes, first re-express them so |
55,455 | How do you calculate eigenvector/value of a non-square matrix in R? | According to the man page, svd returns a list with the following elements:
d: a vector containing the singular values of x, of length min(n, p).
u: a matrix whose columns contain the left singular vectors of x, present if nu 0. Dimension c(n, nu).
v: a matrix whose columns contain the right singular vectors of x, pre... | How do you calculate eigenvector/value of a non-square matrix in R? | According to the man page, svd returns a list with the following elements:
d: a vector containing the singular values of x, of length min(n, p).
u: a matrix whose columns contain the left singular v | How do you calculate eigenvector/value of a non-square matrix in R?
According to the man page, svd returns a list with the following elements:
d: a vector containing the singular values of x, of length min(n, p).
u: a matrix whose columns contain the left singular vectors of x, present if nu 0. Dimension c(n, nu).
v:... | How do you calculate eigenvector/value of a non-square matrix in R?
According to the man page, svd returns a list with the following elements:
d: a vector containing the singular values of x, of length min(n, p).
u: a matrix whose columns contain the left singular v |
55,456 | One-sided Fisher's exact test and its complement | The particular table helps a lot. The Fisher's exact test assigns probabilities to tables with these particular marginals using the hypergeometric distribution. In this case, we're thinking of drawing 9 balls from an urn (the cases) with 2852 white balls (exposed) and 2861 black balls (not exposed). The number of wh... | One-sided Fisher's exact test and its complement | The particular table helps a lot. The Fisher's exact test assigns probabilities to tables with these particular marginals using the hypergeometric distribution. In this case, we're thinking of drawi | One-sided Fisher's exact test and its complement
The particular table helps a lot. The Fisher's exact test assigns probabilities to tables with these particular marginals using the hypergeometric distribution. In this case, we're thinking of drawing 9 balls from an urn (the cases) with 2852 white balls (exposed) and ... | One-sided Fisher's exact test and its complement
The particular table helps a lot. The Fisher's exact test assigns probabilities to tables with these particular marginals using the hypergeometric distribution. In this case, we're thinking of drawi |
55,457 | One-sided Fisher's exact test and its complement | Fisher's test has the null hypothesis that A is the same as B. Suppose that we have 50 observations each for A and B. Fisher's test puts all 100 observations in a bag, shakes it, pulls out 50, calling them A, then pulls out the remaining 50 and calls them B. Calculate the average of A - B. Repeat this permutation many ... | One-sided Fisher's exact test and its complement | Fisher's test has the null hypothesis that A is the same as B. Suppose that we have 50 observations each for A and B. Fisher's test puts all 100 observations in a bag, shakes it, pulls out 50, calling | One-sided Fisher's exact test and its complement
Fisher's test has the null hypothesis that A is the same as B. Suppose that we have 50 observations each for A and B. Fisher's test puts all 100 observations in a bag, shakes it, pulls out 50, calling them A, then pulls out the remaining 50 and calls them B. Calculate th... | One-sided Fisher's exact test and its complement
Fisher's test has the null hypothesis that A is the same as B. Suppose that we have 50 observations each for A and B. Fisher's test puts all 100 observations in a bag, shakes it, pulls out 50, calling |
55,458 | Interaction suppresses the main effect? How to interpret it? | The main effects went from "significant" to "not", but the evidence really didn't change all that much. For example, p=0.047 to p=0.063 for rok isn't, to me, a remarkable change. And a lack of evidence for a coefficient being non-zero isn't the same as saying it is 0.
In considering the coefficient for obdobinehn whe... | Interaction suppresses the main effect? How to interpret it? | The main effects went from "significant" to "not", but the evidence really didn't change all that much. For example, p=0.047 to p=0.063 for rok isn't, to me, a remarkable change. And a lack of evide | Interaction suppresses the main effect? How to interpret it?
The main effects went from "significant" to "not", but the evidence really didn't change all that much. For example, p=0.047 to p=0.063 for rok isn't, to me, a remarkable change. And a lack of evidence for a coefficient being non-zero isn't the same as sayi... | Interaction suppresses the main effect? How to interpret it?
The main effects went from "significant" to "not", but the evidence really didn't change all that much. For example, p=0.047 to p=0.063 for rok isn't, to me, a remarkable change. And a lack of evide |
55,459 | Interaction suppresses the main effect? How to interpret it? | You'll notice that in your ANOVA's (deviance tables) of the models there is no difference in the main effects with, or without the interaction. You don't have to know how to interpret the deviance table, just recognize that there's no difference!
Keep in mind that your "Estimate" column in the regression is about th... | Interaction suppresses the main effect? How to interpret it? | You'll notice that in your ANOVA's (deviance tables) of the models there is no difference in the main effects with, or without the interaction. You don't have to know how to interpret the deviance ta | Interaction suppresses the main effect? How to interpret it?
You'll notice that in your ANOVA's (deviance tables) of the models there is no difference in the main effects with, or without the interaction. You don't have to know how to interpret the deviance table, just recognize that there's no difference!
Keep in m... | Interaction suppresses the main effect? How to interpret it?
You'll notice that in your ANOVA's (deviance tables) of the models there is no difference in the main effects with, or without the interaction. You don't have to know how to interpret the deviance ta |
55,460 | Automatic text quality grading | There are two basic components, technical analysis (grammar, sentence length, etc) and machine learning/statistical analysis.
The best (IMHO) papers on this are those involving automated essay grading. It's a little dated, but this paper covers the techniques employed by all of the major vendors.
ETS (the people behin... | Automatic text quality grading | There are two basic components, technical analysis (grammar, sentence length, etc) and machine learning/statistical analysis.
The best (IMHO) papers on this are those involving automated essay grading | Automatic text quality grading
There are two basic components, technical analysis (grammar, sentence length, etc) and machine learning/statistical analysis.
The best (IMHO) papers on this are those involving automated essay grading. It's a little dated, but this paper covers the techniques employed by all of the major... | Automatic text quality grading
There are two basic components, technical analysis (grammar, sentence length, etc) and machine learning/statistical analysis.
The best (IMHO) papers on this are those involving automated essay grading |
55,461 | Automatic text quality grading | Actually if you want to judge the quality of works, you may want to approach this with something like Google Pagerank. So for example if a particular paper is footnoted or referenced in many other papers then there is a greater chance that it is a well respected paper than a paper that is never footnoted.
Another facto... | Automatic text quality grading | Actually if you want to judge the quality of works, you may want to approach this with something like Google Pagerank. So for example if a particular paper is footnoted or referenced in many other pap | Automatic text quality grading
Actually if you want to judge the quality of works, you may want to approach this with something like Google Pagerank. So for example if a particular paper is footnoted or referenced in many other papers then there is a greater chance that it is a well respected paper than a paper that is... | Automatic text quality grading
Actually if you want to judge the quality of works, you may want to approach this with something like Google Pagerank. So for example if a particular paper is footnoted or referenced in many other pap |
55,462 | Automatic text quality grading | More a comment than an answer, but I suspect that trying to capture the quality of a particular document in a single (non-adaptive) score is the canonical example of YMMV.
For example, in a technical context users might consider quality to mean in-depth treatment of a specific problem, a survey of different techniques,... | Automatic text quality grading | More a comment than an answer, but I suspect that trying to capture the quality of a particular document in a single (non-adaptive) score is the canonical example of YMMV.
For example, in a technical | Automatic text quality grading
More a comment than an answer, but I suspect that trying to capture the quality of a particular document in a single (non-adaptive) score is the canonical example of YMMV.
For example, in a technical context users might consider quality to mean in-depth treatment of a specific problem, a ... | Automatic text quality grading
More a comment than an answer, but I suspect that trying to capture the quality of a particular document in a single (non-adaptive) score is the canonical example of YMMV.
For example, in a technical |
55,463 | When using stratified randomization in a RCT, how many envelopes per stratum should be prepared? | There are no consequences to having too many random assignments left over at the end, and pretty large consequences to having your randomization scheme fail mid-assignment for a trial. Given the cost of an envelop and a piece of paper, I'd vote on the side of safety and make 300 of each.
If you're trying to trim the bu... | When using stratified randomization in a RCT, how many envelopes per stratum should be prepared? | There are no consequences to having too many random assignments left over at the end, and pretty large consequences to having your randomization scheme fail mid-assignment for a trial. Given the cost | When using stratified randomization in a RCT, how many envelopes per stratum should be prepared?
There are no consequences to having too many random assignments left over at the end, and pretty large consequences to having your randomization scheme fail mid-assignment for a trial. Given the cost of an envelop and a pie... | When using stratified randomization in a RCT, how many envelopes per stratum should be prepared?
There are no consequences to having too many random assignments left over at the end, and pretty large consequences to having your randomization scheme fail mid-assignment for a trial. Given the cost |
55,464 | When using stratified randomization in a RCT, how many envelopes per stratum should be prepared? | The stratum populations have a multinomial distribution. 300 is large enough that the Normal approximation will be accurate, implying the population in any given stratum has a Normal$(100, \sqrt{\frac{1}{3}\frac{2}{3}300})$ distribution. Although all three counts are correlated, it's still a good approximation to ass... | When using stratified randomization in a RCT, how many envelopes per stratum should be prepared? | The stratum populations have a multinomial distribution. 300 is large enough that the Normal approximation will be accurate, implying the population in any given stratum has a Normal$(100, \sqrt{\fra | When using stratified randomization in a RCT, how many envelopes per stratum should be prepared?
The stratum populations have a multinomial distribution. 300 is large enough that the Normal approximation will be accurate, implying the population in any given stratum has a Normal$(100, \sqrt{\frac{1}{3}\frac{2}{3}300})... | When using stratified randomization in a RCT, how many envelopes per stratum should be prepared?
The stratum populations have a multinomial distribution. 300 is large enough that the Normal approximation will be accurate, implying the population in any given stratum has a Normal$(100, \sqrt{\fra |
55,465 | Is SPSS giving the wrong residuals for a repeated measures design? | SPSS is giving the residuals from the group means without correcting for individual error.
> my.lm <- lm(value ~ group, DFlong)
> round(matrix(residuals(my.lm),ncol=2),2)
[,1] [,2]
[1,] -3.78 -4.78
[2,] 0.27 1.76
[3,] -4.82 3.02
[4,] 7.33 2.20
[5,] 1.00 -2.20
The residuals after correcting for individual ... | Is SPSS giving the wrong residuals for a repeated measures design? | SPSS is giving the residuals from the group means without correcting for individual error.
> my.lm <- lm(value ~ group, DFlong)
> round(matrix(residuals(my.lm),ncol=2),2)
[,1] [,2]
[1,] -3.78 - | Is SPSS giving the wrong residuals for a repeated measures design?
SPSS is giving the residuals from the group means without correcting for individual error.
> my.lm <- lm(value ~ group, DFlong)
> round(matrix(residuals(my.lm),ncol=2),2)
[,1] [,2]
[1,] -3.78 -4.78
[2,] 0.27 1.76
[3,] -4.82 3.02
[4,] 7.33 2.... | Is SPSS giving the wrong residuals for a repeated measures design?
SPSS is giving the residuals from the group means without correcting for individual error.
> my.lm <- lm(value ~ group, DFlong)
> round(matrix(residuals(my.lm),ncol=2),2)
[,1] [,2]
[1,] -3.78 - |
55,466 | Is SPSS giving the wrong residuals for a repeated measures design? | I've read more now on the repeated measures general linear model, and can explain how the residuals are calculated that R is reporting in my.aov.
A repeated measures ANOVA is applied to a transformation of the within-subject variables, and the residuals of this model are the ones returned from the aov fit.
The transfor... | Is SPSS giving the wrong residuals for a repeated measures design? | I've read more now on the repeated measures general linear model, and can explain how the residuals are calculated that R is reporting in my.aov.
A repeated measures ANOVA is applied to a transformati | Is SPSS giving the wrong residuals for a repeated measures design?
I've read more now on the repeated measures general linear model, and can explain how the residuals are calculated that R is reporting in my.aov.
A repeated measures ANOVA is applied to a transformation of the within-subject variables, and the residuals... | Is SPSS giving the wrong residuals for a repeated measures design?
I've read more now on the repeated measures general linear model, and can explain how the residuals are calculated that R is reporting in my.aov.
A repeated measures ANOVA is applied to a transformati |
55,467 | Prediction with GLS | Suppose we have a GLS model:
$$y=X\beta+u,$$
with
$$Euu'=\Omega.$$
Suppose we want to predict $y^*$:
$$y^*=x^*\beta+u^*,$$
Goldberger proved that the best linear unbiased prediction for $y^*$ is the following:
$$\hat{y}=x^*\hat{\beta}+w'\Omega^{-1}\hat{u},$$
where
$$\hat\beta=(X'\Omega^{-1}X)^{-1}X\Omega^{-1}y,\quad \... | Prediction with GLS | Suppose we have a GLS model:
$$y=X\beta+u,$$
with
$$Euu'=\Omega.$$
Suppose we want to predict $y^*$:
$$y^*=x^*\beta+u^*,$$
Goldberger proved that the best linear unbiased prediction for $y^*$ is the f | Prediction with GLS
Suppose we have a GLS model:
$$y=X\beta+u,$$
with
$$Euu'=\Omega.$$
Suppose we want to predict $y^*$:
$$y^*=x^*\beta+u^*,$$
Goldberger proved that the best linear unbiased prediction for $y^*$ is the following:
$$\hat{y}=x^*\hat{\beta}+w'\Omega^{-1}\hat{u},$$
where
$$\hat\beta=(X'\Omega^{-1}X)^{-1}X... | Prediction with GLS
Suppose we have a GLS model:
$$y=X\beta+u,$$
with
$$Euu'=\Omega.$$
Suppose we want to predict $y^*$:
$$y^*=x^*\beta+u^*,$$
Goldberger proved that the best linear unbiased prediction for $y^*$ is the f |
55,468 | Prediction with GLS | The $\beta$'s that come out of GLS are estimates of the predictor effects that contribute to the mean response and the assumption of GLS is that the mean of $Y|X$ is the same for each data point - only non-constant variance and serial correlation are allowed for. So, to predict a new data point, yes, you would just plu... | Prediction with GLS | The $\beta$'s that come out of GLS are estimates of the predictor effects that contribute to the mean response and the assumption of GLS is that the mean of $Y|X$ is the same for each data point - onl | Prediction with GLS
The $\beta$'s that come out of GLS are estimates of the predictor effects that contribute to the mean response and the assumption of GLS is that the mean of $Y|X$ is the same for each data point - only non-constant variance and serial correlation are allowed for. So, to predict a new data point, yes... | Prediction with GLS
The $\beta$'s that come out of GLS are estimates of the predictor effects that contribute to the mean response and the assumption of GLS is that the mean of $Y|X$ is the same for each data point - onl |
55,469 | Zero regression coefficient when correlations are not zero | Yes, that is possible. Take these data for example
x y xy
.2217 .5000 .1108
.3048 -.9787 -.2983
-1.6445 .3512 -.5775
-.2461 -.4866 .1197
-.3170 -.0954 .0302
-1.1603 1.8352 -2.1294
-.8720 .1372 -.1196
-1.7852 -.2160 .3856
1.0100 .0165 .0166
.3000 -.3251 -.0975
$XY$... | Zero regression coefficient when correlations are not zero | Yes, that is possible. Take these data for example
x y xy
.2217 .5000 .1108
.3048 -.9787 -.2983
-1.6445 .3512 -.5775
-.2461 -.4866 .1197
-.3170 -.0954 .0302
-1.16 | Zero regression coefficient when correlations are not zero
Yes, that is possible. Take these data for example
x y xy
.2217 .5000 .1108
.3048 -.9787 -.2983
-1.6445 .3512 -.5775
-.2461 -.4866 .1197
-.3170 -.0954 .0302
-1.1603 1.8352 -2.1294
-.8720 .1372 -.1196
-1.7852 -.2160 ... | Zero regression coefficient when correlations are not zero
Yes, that is possible. Take these data for example
x y xy
.2217 .5000 .1108
.3048 -.9787 -.2983
-1.6445 .3512 -.5775
-.2461 -.4866 .1197
-.3170 -.0954 .0302
-1.16 |
55,470 | What techniques are used for empirical, stochastic simulation of a time series? | Would dynamic linear models be applicable? (State Space formulation, Kalman filter, etc.) The dlm package has some nice tools to create and simulate from models. | What techniques are used for empirical, stochastic simulation of a time series? | Would dynamic linear models be applicable? (State Space formulation, Kalman filter, etc.) The dlm package has some nice tools to create and simulate from models. | What techniques are used for empirical, stochastic simulation of a time series?
Would dynamic linear models be applicable? (State Space formulation, Kalman filter, etc.) The dlm package has some nice tools to create and simulate from models. | What techniques are used for empirical, stochastic simulation of a time series?
Would dynamic linear models be applicable? (State Space formulation, Kalman filter, etc.) The dlm package has some nice tools to create and simulate from models. |
55,471 | What techniques are used for empirical, stochastic simulation of a time series? | Conceptually, the question fits into the framework of functional data analysis, see, for instance Applied Functional Data Analysis by Ramsay and Silverman. The usual assumption here is that we have a data set of independent, and perhaps even identically distributed, smooth curves. Fitting an fda model to your data over... | What techniques are used for empirical, stochastic simulation of a time series? | Conceptually, the question fits into the framework of functional data analysis, see, for instance Applied Functional Data Analysis by Ramsay and Silverman. The usual assumption here is that we have a | What techniques are used for empirical, stochastic simulation of a time series?
Conceptually, the question fits into the framework of functional data analysis, see, for instance Applied Functional Data Analysis by Ramsay and Silverman. The usual assumption here is that we have a data set of independent, and perhaps eve... | What techniques are used for empirical, stochastic simulation of a time series?
Conceptually, the question fits into the framework of functional data analysis, see, for instance Applied Functional Data Analysis by Ramsay and Silverman. The usual assumption here is that we have a |
55,472 | How to derive a k-means objective function in matrix form? | Given an $m$ by $n$ matrix $X$, the algorithm seeks to group its $n$ columns, thought of as $m$-vectors, into a specified number of groups, $k$. This can be represented by an $n$ by $k$ matrix $A$ having entries in $\{0,1\}$ and one column for each of the $k$ groups. Column $j$ indicates which vectors in $X$ belong t... | How to derive a k-means objective function in matrix form? | Given an $m$ by $n$ matrix $X$, the algorithm seeks to group its $n$ columns, thought of as $m$-vectors, into a specified number of groups, $k$. This can be represented by an $n$ by $k$ matrix $A$ ha | How to derive a k-means objective function in matrix form?
Given an $m$ by $n$ matrix $X$, the algorithm seeks to group its $n$ columns, thought of as $m$-vectors, into a specified number of groups, $k$. This can be represented by an $n$ by $k$ matrix $A$ having entries in $\{0,1\}$ and one column for each of the $k$ ... | How to derive a k-means objective function in matrix form?
Given an $m$ by $n$ matrix $X$, the algorithm seeks to group its $n$ columns, thought of as $m$-vectors, into a specified number of groups, $k$. This can be represented by an $n$ by $k$ matrix $A$ ha |
55,473 | How do you use draws from a Markov chain (Monte Carlo) for (bayesian) inference? | Bayesian Statistics does not depend on Markov chains (well in theory), Markov chain Monte Carlo is a method for making the computations in Bayesian Statistics easier (doable). Generally we want to generate data from the posterior distribution which we can easily compute parts of, but not always all of (the normalizing... | How do you use draws from a Markov chain (Monte Carlo) for (bayesian) inference? | Bayesian Statistics does not depend on Markov chains (well in theory), Markov chain Monte Carlo is a method for making the computations in Bayesian Statistics easier (doable). Generally we want to ge | How do you use draws from a Markov chain (Monte Carlo) for (bayesian) inference?
Bayesian Statistics does not depend on Markov chains (well in theory), Markov chain Monte Carlo is a method for making the computations in Bayesian Statistics easier (doable). Generally we want to generate data from the posterior distribu... | How do you use draws from a Markov chain (Monte Carlo) for (bayesian) inference?
Bayesian Statistics does not depend on Markov chains (well in theory), Markov chain Monte Carlo is a method for making the computations in Bayesian Statistics easier (doable). Generally we want to ge |
55,474 | Multiple imputation on single subscale item or subscale scores? | I basically concur with everything wolf.rauch said here, and would like to discuss some alternatives that might be available to you.
My understanding is that AMOS had had FIML (full information maximum likelihood) for continuous data for at least ten years before it was acquired by IBM -- see http://www.smallwaters.com... | Multiple imputation on single subscale item or subscale scores? | I basically concur with everything wolf.rauch said here, and would like to discuss some alternatives that might be available to you.
My understanding is that AMOS had had FIML (full information maximu | Multiple imputation on single subscale item or subscale scores?
I basically concur with everything wolf.rauch said here, and would like to discuss some alternatives that might be available to you.
My understanding is that AMOS had had FIML (full information maximum likelihood) for continuous data for at least ten years... | Multiple imputation on single subscale item or subscale scores?
I basically concur with everything wolf.rauch said here, and would like to discuss some alternatives that might be available to you.
My understanding is that AMOS had had FIML (full information maximu |
55,475 | Multiple imputation on single subscale item or subscale scores? | Q1: I suppose you're not asking whether the variable names have anything to do with imputation. I suppose you're asking whether to do multiple imputation on your raw data or on "scales" which means sum scores of raw data variables which are assumed to measure the same construct.
Given that you are concerned about the ... | Multiple imputation on single subscale item or subscale scores? | Q1: I suppose you're not asking whether the variable names have anything to do with imputation. I suppose you're asking whether to do multiple imputation on your raw data or on "scales" which means su | Multiple imputation on single subscale item or subscale scores?
Q1: I suppose you're not asking whether the variable names have anything to do with imputation. I suppose you're asking whether to do multiple imputation on your raw data or on "scales" which means sum scores of raw data variables which are assumed to meas... | Multiple imputation on single subscale item or subscale scores?
Q1: I suppose you're not asking whether the variable names have anything to do with imputation. I suppose you're asking whether to do multiple imputation on your raw data or on "scales" which means su |
55,476 | Multiple imputation on single subscale item or subscale scores? | There's a great article comparing item-level vs. scale-level imputation by Craig Enders and colleagues (see here; sadly, one needs institutional access).
Basically, the authors conclude that both item-level and scale-level imputation are similar in the level of bias they introduce in scale estimates, but do differ in t... | Multiple imputation on single subscale item or subscale scores? | There's a great article comparing item-level vs. scale-level imputation by Craig Enders and colleagues (see here; sadly, one needs institutional access).
Basically, the authors conclude that both item | Multiple imputation on single subscale item or subscale scores?
There's a great article comparing item-level vs. scale-level imputation by Craig Enders and colleagues (see here; sadly, one needs institutional access).
Basically, the authors conclude that both item-level and scale-level imputation are similar in the lev... | Multiple imputation on single subscale item or subscale scores?
There's a great article comparing item-level vs. scale-level imputation by Craig Enders and colleagues (see here; sadly, one needs institutional access).
Basically, the authors conclude that both item |
55,477 | Bayesian analysis of data | If you just want to predict $Y$ then an explicit joint model for $X$ and $Y$ when $X$ is 4 dimensional is probably overkill. This is especially true if you truly don't have any information about the distribution of the $X_i$'s or the relationship between $Y$ and $X$. An exception would be if there is significant missin... | Bayesian analysis of data | If you just want to predict $Y$ then an explicit joint model for $X$ and $Y$ when $X$ is 4 dimensional is probably overkill. This is especially true if you truly don't have any information about the d | Bayesian analysis of data
If you just want to predict $Y$ then an explicit joint model for $X$ and $Y$ when $X$ is 4 dimensional is probably overkill. This is especially true if you truly don't have any information about the distribution of the $X_i$'s or the relationship between $Y$ and $X$. An exception would be if t... | Bayesian analysis of data
If you just want to predict $Y$ then an explicit joint model for $X$ and $Y$ when $X$ is 4 dimensional is probably overkill. This is especially true if you truly don't have any information about the d |
55,478 | Looking for a name for a "mean-influencing" statistic | The mean is the coefficient in the regression of the data against the constant $1$. Your statistic, in this regression context, is the simplest possible example of the DFBETA diagnostic defined in Belsley, Kuh, & Welsch, Regression Diagnostics (J Wiley & Sons, 1980):
...we look first at the change in the estimated re... | Looking for a name for a "mean-influencing" statistic | The mean is the coefficient in the regression of the data against the constant $1$. Your statistic, in this regression context, is the simplest possible example of the DFBETA diagnostic defined in Be | Looking for a name for a "mean-influencing" statistic
The mean is the coefficient in the regression of the data against the constant $1$. Your statistic, in this regression context, is the simplest possible example of the DFBETA diagnostic defined in Belsley, Kuh, & Welsch, Regression Diagnostics (J Wiley & Sons, 1980... | Looking for a name for a "mean-influencing" statistic
The mean is the coefficient in the regression of the data against the constant $1$. Your statistic, in this regression context, is the simplest possible example of the DFBETA diagnostic defined in Be |
55,479 | Looking for a name for a "mean-influencing" statistic | It's close to (if not exactly) local influence and/or Cook's distance.
JRSS B, Vol. 48, No. 2, 1986, p.133-169 is the classic paper. A bit dense but a place to start looking in the literature. | Looking for a name for a "mean-influencing" statistic | It's close to (if not exactly) local influence and/or Cook's distance.
JRSS B, Vol. 48, No. 2, 1986, p.133-169 is the classic paper. A bit dense but a place to start looking in the literature. | Looking for a name for a "mean-influencing" statistic
It's close to (if not exactly) local influence and/or Cook's distance.
JRSS B, Vol. 48, No. 2, 1986, p.133-169 is the classic paper. A bit dense but a place to start looking in the literature. | Looking for a name for a "mean-influencing" statistic
It's close to (if not exactly) local influence and/or Cook's distance.
JRSS B, Vol. 48, No. 2, 1986, p.133-169 is the classic paper. A bit dense but a place to start looking in the literature. |
55,480 | Covariates in regression models | Correlation with the dependent variable is a definite plus (especially for linear regression where there are close links between the coefficients and covariance with the dependent variable).
Correlation with the other covariates/predictors is somewhat more subtle and depends on your goal. Generally, it is considered go... | Covariates in regression models | Correlation with the dependent variable is a definite plus (especially for linear regression where there are close links between the coefficients and covariance with the dependent variable).
Correlati | Covariates in regression models
Correlation with the dependent variable is a definite plus (especially for linear regression where there are close links between the coefficients and covariance with the dependent variable).
Correlation with the other covariates/predictors is somewhat more subtle and depends on your goal... | Covariates in regression models
Correlation with the dependent variable is a definite plus (especially for linear regression where there are close links between the coefficients and covariance with the dependent variable).
Correlati |
55,481 | Covariates in regression models | This really depends on the scientific question being asked. If you are interested in if there is a relationship between x1 and y, then do the regression between x1 and y. If you are interested in if x1 helps predict y above and beyond the effects of x2, x3, etc. then you need to include the other x's in the model.
Fo... | Covariates in regression models | This really depends on the scientific question being asked. If you are interested in if there is a relationship between x1 and y, then do the regression between x1 and y. If you are interested in if | Covariates in regression models
This really depends on the scientific question being asked. If you are interested in if there is a relationship between x1 and y, then do the regression between x1 and y. If you are interested in if x1 helps predict y above and beyond the effects of x2, x3, etc. then you need to includ... | Covariates in regression models
This really depends on the scientific question being asked. If you are interested in if there is a relationship between x1 and y, then do the regression between x1 and y. If you are interested in if |
55,482 | Sliding window validation for time series | Your understanding about sliding window analysis is generally correct. You may find it helpful to separate the model validation process from the actual forecasting. In model validation, you use $k$ instances to train a model that predicts "one step" forward. Make sure each of your $k$ instances uses only information av... | Sliding window validation for time series | Your understanding about sliding window analysis is generally correct. You may find it helpful to separate the model validation process from the actual forecasting. In model validation, you use $k$ in | Sliding window validation for time series
Your understanding about sliding window analysis is generally correct. You may find it helpful to separate the model validation process from the actual forecasting. In model validation, you use $k$ instances to train a model that predicts "one step" forward. Make sure each of y... | Sliding window validation for time series
Your understanding about sliding window analysis is generally correct. You may find it helpful to separate the model validation process from the actual forecasting. In model validation, you use $k$ in |
55,483 | How can I generate correlated timeseries made up of 0s and 1s? | A standard method is to begin by generating an autocorrelated Gaussian process $z_i$. (It doesn't have to be Gaussian, but such processes are easy to generate.) Take the logistic (inverse logit) of the values, producing a series of numbers $p_i = 1/\left(1 + \exp(-z_i)\right)$ in the interval $(0,1)$. Independently ... | How can I generate correlated timeseries made up of 0s and 1s? | A standard method is to begin by generating an autocorrelated Gaussian process $z_i$. (It doesn't have to be Gaussian, but such processes are easy to generate.) Take the logistic (inverse logit) of | How can I generate correlated timeseries made up of 0s and 1s?
A standard method is to begin by generating an autocorrelated Gaussian process $z_i$. (It doesn't have to be Gaussian, but such processes are easy to generate.) Take the logistic (inverse logit) of the values, producing a series of numbers $p_i = 1/\left(... | How can I generate correlated timeseries made up of 0s and 1s?
A standard method is to begin by generating an autocorrelated Gaussian process $z_i$. (It doesn't have to be Gaussian, but such processes are easy to generate.) Take the logistic (inverse logit) of |
55,484 | Assessing rater bias where one rater has given one very high rating and the remainder very low ratings | Overview
Usually when I think of multiple raters assessing multiple objects, I think of "bias" as a mean difference in expected rating of a particular judge from the mean of a hypothetical population of judges.
This is a rather statistical definition of bias, which does not necessarily correspond to everyday definition... | Assessing rater bias where one rater has given one very high rating and the remainder very low ratin | Overview
Usually when I think of multiple raters assessing multiple objects, I think of "bias" as a mean difference in expected rating of a particular judge from the mean of a hypothetical population | Assessing rater bias where one rater has given one very high rating and the remainder very low ratings
Overview
Usually when I think of multiple raters assessing multiple objects, I think of "bias" as a mean difference in expected rating of a particular judge from the mean of a hypothetical population of judges.
This i... | Assessing rater bias where one rater has given one very high rating and the remainder very low ratin
Overview
Usually when I think of multiple raters assessing multiple objects, I think of "bias" as a mean difference in expected rating of a particular judge from the mean of a hypothetical population |
55,485 | Assessing rater bias where one rater has given one very high rating and the remainder very low ratings | You could measure agreement in ratings across judges with inter-rater reliability statistics. This would tell you whether the judging of contestants is consistent across judges.
There may be a more sophisticated way of doing this, but I might naively try dropping out each of the five judges individually looking at how ... | Assessing rater bias where one rater has given one very high rating and the remainder very low ratin | You could measure agreement in ratings across judges with inter-rater reliability statistics. This would tell you whether the judging of contestants is consistent across judges.
There may be a more so | Assessing rater bias where one rater has given one very high rating and the remainder very low ratings
You could measure agreement in ratings across judges with inter-rater reliability statistics. This would tell you whether the judging of contestants is consistent across judges.
There may be a more sophisticated way o... | Assessing rater bias where one rater has given one very high rating and the remainder very low ratin
You could measure agreement in ratings across judges with inter-rater reliability statistics. This would tell you whether the judging of contestants is consistent across judges.
There may be a more so |
55,486 | Assessing rater bias where one rater has given one very high rating and the remainder very low ratings | You won't be able to demonstrate bias, but you can try to establish whether the 96.05 is an outlier using Dixon's Test for Outliers. If these judges went on to judge these same contestants on another task/domain, you could test for the replicability of this unusual result for Judge 5 and Contestant 4. | Assessing rater bias where one rater has given one very high rating and the remainder very low ratin | You won't be able to demonstrate bias, but you can try to establish whether the 96.05 is an outlier using Dixon's Test for Outliers. If these judges went on to judge these same contestants on another | Assessing rater bias where one rater has given one very high rating and the remainder very low ratings
You won't be able to demonstrate bias, but you can try to establish whether the 96.05 is an outlier using Dixon's Test for Outliers. If these judges went on to judge these same contestants on another task/domain, you... | Assessing rater bias where one rater has given one very high rating and the remainder very low ratin
You won't be able to demonstrate bias, but you can try to establish whether the 96.05 is an outlier using Dixon's Test for Outliers. If these judges went on to judge these same contestants on another |
55,487 | Assessing rater bias where one rater has given one very high rating and the remainder very low ratings | You could think of this as a test of variances. Judge 5's scores will get more weight because the variability of the scores is higher.
This test would be for the equality of two variances. It's in most intro stat books, and even in Excel, which provides the following results for judge 5 versus judge 1-4
F-Test Two-Sam... | Assessing rater bias where one rater has given one very high rating and the remainder very low ratin | You could think of this as a test of variances. Judge 5's scores will get more weight because the variability of the scores is higher.
This test would be for the equality of two variances. It's in mo | Assessing rater bias where one rater has given one very high rating and the remainder very low ratings
You could think of this as a test of variances. Judge 5's scores will get more weight because the variability of the scores is higher.
This test would be for the equality of two variances. It's in most intro stat boo... | Assessing rater bias where one rater has given one very high rating and the remainder very low ratin
You could think of this as a test of variances. Judge 5's scores will get more weight because the variability of the scores is higher.
This test would be for the equality of two variances. It's in mo |
55,488 | Expected value of a transformation | Yes. In general if $X\sim f(x)$ then for a function $g(x)$ you have $E(g(X)) = \int g(x)f(x)dx$. You can verify this for simple cases by deriving the distribution of the transformed variable. The completely general result takes some more advanced math which you can probably safely avoid :) | Expected value of a transformation | Yes. In general if $X\sim f(x)$ then for a function $g(x)$ you have $E(g(X)) = \int g(x)f(x)dx$. You can verify this for simple cases by deriving the distribution of the transformed variable. The comp | Expected value of a transformation
Yes. In general if $X\sim f(x)$ then for a function $g(x)$ you have $E(g(X)) = \int g(x)f(x)dx$. You can verify this for simple cases by deriving the distribution of the transformed variable. The completely general result takes some more advanced math which you can probably safely avo... | Expected value of a transformation
Yes. In general if $X\sim f(x)$ then for a function $g(x)$ you have $E(g(X)) = \int g(x)f(x)dx$. You can verify this for simple cases by deriving the distribution of the transformed variable. The comp |
55,489 | Expected value of a transformation | Another approach if you are happy with a numerical estimate (as opposed to the theorectical exact value) is to generate a bunch of data from the distribution, do the transformation, then take the mean of the transformed data as the estimate of the expected value. This avoids integration which can be nice in ugly cases... | Expected value of a transformation | Another approach if you are happy with a numerical estimate (as opposed to the theorectical exact value) is to generate a bunch of data from the distribution, do the transformation, then take the mean | Expected value of a transformation
Another approach if you are happy with a numerical estimate (as opposed to the theorectical exact value) is to generate a bunch of data from the distribution, do the transformation, then take the mean of the transformed data as the estimate of the expected value. This avoids integrat... | Expected value of a transformation
Another approach if you are happy with a numerical estimate (as opposed to the theorectical exact value) is to generate a bunch of data from the distribution, do the transformation, then take the mean |
55,490 | Calculating the distribution of maximal value of $n$ draws from a normal distribution [duplicate] | If $\zeta_j = \sigma \xi_j $ for some $\sigma >0$ and some $\mu$ then
$$E[\max|\zeta_j|] = E[\max|\sigma \xi_j |] = E[\sigma \max| \xi_j|]= \sigma E[ \max| \xi_j|]$$
and this tells us how to move from a standard normal with mean $0$ and standard deviation $1$ to a normal distribution with mean $0$ and standard deviat... | Calculating the distribution of maximal value of $n$ draws from a normal distribution [duplicate] | If $\zeta_j = \sigma \xi_j $ for some $\sigma >0$ and some $\mu$ then
$$E[\max|\zeta_j|] = E[\max|\sigma \xi_j |] = E[\sigma \max| \xi_j|]= \sigma E[ \max| \xi_j|]$$
and this tells us how to move fr | Calculating the distribution of maximal value of $n$ draws from a normal distribution [duplicate]
If $\zeta_j = \sigma \xi_j $ for some $\sigma >0$ and some $\mu$ then
$$E[\max|\zeta_j|] = E[\max|\sigma \xi_j |] = E[\sigma \max| \xi_j|]= \sigma E[ \max| \xi_j|]$$
and this tells us how to move from a standard normal w... | Calculating the distribution of maximal value of $n$ draws from a normal distribution [duplicate]
If $\zeta_j = \sigma \xi_j $ for some $\sigma >0$ and some $\mu$ then
$$E[\max|\zeta_j|] = E[\max|\sigma \xi_j |] = E[\sigma \max| \xi_j|]= \sigma E[ \max| \xi_j|]$$
and this tells us how to move fr |
55,491 | Calculating the distribution of maximal value of $n$ draws from a normal distribution [duplicate] | Intuitively: values of the standard normal distribution (including the absolute maximum) 'tend to be' 1 SD = 1 away from 0.
In non-standard zero-mean normal, the data 'tend to be' 1 SD = sigma away from 0.
You could say that as long as you're doing linear stuff, all distances to zero will blow up by a factor sigma. | Calculating the distribution of maximal value of $n$ draws from a normal distribution [duplicate] | Intuitively: values of the standard normal distribution (including the absolute maximum) 'tend to be' 1 SD = 1 away from 0.
In non-standard zero-mean normal, the data 'tend to be' 1 SD = sigma away fr | Calculating the distribution of maximal value of $n$ draws from a normal distribution [duplicate]
Intuitively: values of the standard normal distribution (including the absolute maximum) 'tend to be' 1 SD = 1 away from 0.
In non-standard zero-mean normal, the data 'tend to be' 1 SD = sigma away from 0.
You could say th... | Calculating the distribution of maximal value of $n$ draws from a normal distribution [duplicate]
Intuitively: values of the standard normal distribution (including the absolute maximum) 'tend to be' 1 SD = 1 away from 0.
In non-standard zero-mean normal, the data 'tend to be' 1 SD = sigma away fr |
55,492 | Counterexample for interaction and parallel curves? | This depends on what is meant by "interaction". If the data have no noise - the plot is literally just two parallel lines, then there is certainly no interaction, we know this deductively, without any need for statistics. Secondly if the lines are not parallel, then we know deductively that there is interaction. So ... | Counterexample for interaction and parallel curves? | This depends on what is meant by "interaction". If the data have no noise - the plot is literally just two parallel lines, then there is certainly no interaction, we know this deductively, without an | Counterexample for interaction and parallel curves?
This depends on what is meant by "interaction". If the data have no noise - the plot is literally just two parallel lines, then there is certainly no interaction, we know this deductively, without any need for statistics. Secondly if the lines are not parallel, then... | Counterexample for interaction and parallel curves?
This depends on what is meant by "interaction". If the data have no noise - the plot is literally just two parallel lines, then there is certainly no interaction, we know this deductively, without an |
55,493 | Counterexample for interaction and parallel curves? | To me it seems like you (and many books probably) are confusing the empirical level with the theoretical level: The null hypothesis of an interaction effect in a two-way ANOVA is defined on the theoretical level using the cell expected values $\mu_{jk}$ (and not response values): there is an interaction if (and only if... | Counterexample for interaction and parallel curves? | To me it seems like you (and many books probably) are confusing the empirical level with the theoretical level: The null hypothesis of an interaction effect in a two-way ANOVA is defined on the theore | Counterexample for interaction and parallel curves?
To me it seems like you (and many books probably) are confusing the empirical level with the theoretical level: The null hypothesis of an interaction effect in a two-way ANOVA is defined on the theoretical level using the cell expected values $\mu_{jk}$ (and not respo... | Counterexample for interaction and parallel curves?
To me it seems like you (and many books probably) are confusing the empirical level with the theoretical level: The null hypothesis of an interaction effect in a two-way ANOVA is defined on the theore |
55,494 | Counterexample for interaction and parallel curves? | Yes, if the true (hypothetical) responses are not parallel there is interaction. Not parallel, however, does not necessarily mean that the segments cross. When you investigate interaction the sampling error may lead to different results in the sample than in the population, so it's useful to calculate confidence interv... | Counterexample for interaction and parallel curves? | Yes, if the true (hypothetical) responses are not parallel there is interaction. Not parallel, however, does not necessarily mean that the segments cross. When you investigate interaction the sampling | Counterexample for interaction and parallel curves?
Yes, if the true (hypothetical) responses are not parallel there is interaction. Not parallel, however, does not necessarily mean that the segments cross. When you investigate interaction the sampling error may lead to different results in the sample than in the popul... | Counterexample for interaction and parallel curves?
Yes, if the true (hypothetical) responses are not parallel there is interaction. Not parallel, however, does not necessarily mean that the segments cross. When you investigate interaction the sampling |
55,495 | Monte Carlo experiment to estimate coverage probability | I disagree with Henry - I think you should be dividing by sqrt(n), because it's a confidence interval for the mean. You also have to add a df = n-1 argument to your qt calls.
And the last line should be mean(LCL < 2 & UCL > 2). This is because 2 is the true mean, and you're interested in the condition that 2 is in the ... | Monte Carlo experiment to estimate coverage probability | I disagree with Henry - I think you should be dividing by sqrt(n), because it's a confidence interval for the mean. You also have to add a df = n-1 argument to your qt calls.
And the last line should | Monte Carlo experiment to estimate coverage probability
I disagree with Henry - I think you should be dividing by sqrt(n), because it's a confidence interval for the mean. You also have to add a df = n-1 argument to your qt calls.
And the last line should be mean(LCL < 2 & UCL > 2). This is because 2 is the true mean, ... | Monte Carlo experiment to estimate coverage probability
I disagree with Henry - I think you should be dividing by sqrt(n), because it's a confidence interval for the mean. You also have to add a df = n-1 argument to your qt calls.
And the last line should |
55,496 | Monte Carlo experiment to estimate coverage probability | You have several issues with your code:
Your mean(UCL < 0 & LCL > 0) is decidedly strange, and in particular is failing because UCL is coming out positive so you are taking the mean of an empty set. A $\chi^2$ distribution takes only positive values.
(since solved) You have UCL less than LCL, which is a slightly odd ... | Monte Carlo experiment to estimate coverage probability | You have several issues with your code:
Your mean(UCL < 0 & LCL > 0) is decidedly strange, and in particular is failing because UCL is coming out positive so you are taking the mean of an empty set. | Monte Carlo experiment to estimate coverage probability
You have several issues with your code:
Your mean(UCL < 0 & LCL > 0) is decidedly strange, and in particular is failing because UCL is coming out positive so you are taking the mean of an empty set. A $\chi^2$ distribution takes only positive values.
(since solv... | Monte Carlo experiment to estimate coverage probability
You have several issues with your code:
Your mean(UCL < 0 & LCL > 0) is decidedly strange, and in particular is failing because UCL is coming out positive so you are taking the mean of an empty set. |
55,497 | Correlation analysis and correcting $p$-values for multiple testing | In my opinion you should test if your variables are distributed normally and chose a suitable test accordingly.
Concerning the correction for alpha inflation: What you are doing is data mining. You have experimental data and now you are digging in it to find ... anything. Do that. But also know that anything you might... | Correlation analysis and correcting $p$-values for multiple testing | In my opinion you should test if your variables are distributed normally and chose a suitable test accordingly.
Concerning the correction for alpha inflation: What you are doing is data mining. You h | Correlation analysis and correcting $p$-values for multiple testing
In my opinion you should test if your variables are distributed normally and chose a suitable test accordingly.
Concerning the correction for alpha inflation: What you are doing is data mining. You have experimental data and now you are digging in it ... | Correlation analysis and correcting $p$-values for multiple testing
In my opinion you should test if your variables are distributed normally and chose a suitable test accordingly.
Concerning the correction for alpha inflation: What you are doing is data mining. You h |
55,498 | Correlation analysis and correcting $p$-values for multiple testing | You want to perform a canonical correlation analysis. This will provide information about correlations among linear combinations of your sets of parameters, potentially uncovering stronger information of the type you seek. The Wikipedia article explains the theory, provides the equations, and presents the appropriate... | Correlation analysis and correcting $p$-values for multiple testing | You want to perform a canonical correlation analysis. This will provide information about correlations among linear combinations of your sets of parameters, potentially uncovering stronger informatio | Correlation analysis and correcting $p$-values for multiple testing
You want to perform a canonical correlation analysis. This will provide information about correlations among linear combinations of your sets of parameters, potentially uncovering stronger information of the type you seek. The Wikipedia article expla... | Correlation analysis and correcting $p$-values for multiple testing
You want to perform a canonical correlation analysis. This will provide information about correlations among linear combinations of your sets of parameters, potentially uncovering stronger informatio |
55,499 | How to correct uneven sampling distribution when calculating the mean? | The answer depends on the characteristics of $f$. Regardless, its average (by definition) is $\frac{1}{100}\int_0^{100}{f(x)dx}$, so your problem is one of estimating that integral from values at a discrete set of points. For a highly discontinuous function you can use any Riemann sum; for a differentiable function u... | How to correct uneven sampling distribution when calculating the mean? | The answer depends on the characteristics of $f$. Regardless, its average (by definition) is $\frac{1}{100}\int_0^{100}{f(x)dx}$, so your problem is one of estimating that integral from values at a d | How to correct uneven sampling distribution when calculating the mean?
The answer depends on the characteristics of $f$. Regardless, its average (by definition) is $\frac{1}{100}\int_0^{100}{f(x)dx}$, so your problem is one of estimating that integral from values at a discrete set of points. For a highly discontinuou... | How to correct uneven sampling distribution when calculating the mean?
The answer depends on the characteristics of $f$. Regardless, its average (by definition) is $\frac{1}{100}\int_0^{100}{f(x)dx}$, so your problem is one of estimating that integral from values at a d |
55,500 | Modeling vacancy rate | One of the tricks in modelling percentages is to use the logit transformation. Then instead of modelling percentage $p_i$ as linear function you model the logit transform of this percentage:
\begin{align}
y_i=\log\frac{p_i}{1-p_i}
\end{align}
In R you will need to create new transformed variable and use it as a depende... | Modeling vacancy rate | One of the tricks in modelling percentages is to use the logit transformation. Then instead of modelling percentage $p_i$ as linear function you model the logit transform of this percentage:
\begin{al | Modeling vacancy rate
One of the tricks in modelling percentages is to use the logit transformation. Then instead of modelling percentage $p_i$ as linear function you model the logit transform of this percentage:
\begin{align}
y_i=\log\frac{p_i}{1-p_i}
\end{align}
In R you will need to create new transformed variable a... | Modeling vacancy rate
One of the tricks in modelling percentages is to use the logit transformation. Then instead of modelling percentage $p_i$ as linear function you model the logit transform of this percentage:
\begin{al |
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