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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51,501 | PCA on train and test datasets: should I run one PCA on train+test or two separate on train and on test? [duplicate] | In the context of this problem, (2) makes more sense, because otherwise you may not even have the same features you are trying to classify (ie reduced dimensions may mean very different things). See here for a more detailed discussion https://stackoverflow.com/questions/10818718/principal-component-analysis | PCA on train and test datasets: should I run one PCA on train+test or two separate on train and on t | In the context of this problem, (2) makes more sense, because otherwise you may not even have the same features you are trying to classify (ie reduced dimensions may mean very different things). See h | PCA on train and test datasets: should I run one PCA on train+test or two separate on train and on test? [duplicate]
In the context of this problem, (2) makes more sense, because otherwise you may not even have the same features you are trying to classify (ie reduced dimensions may mean very different things). See here... | PCA on train and test datasets: should I run one PCA on train+test or two separate on train and on t
In the context of this problem, (2) makes more sense, because otherwise you may not even have the same features you are trying to classify (ie reduced dimensions may mean very different things). See h |
51,502 | PCA on train and test datasets: should I run one PCA on train+test or two separate on train and on test? [duplicate] | (1) is incorrect, because if you run PCA on the two sets separately, you will end up with two different spaces. You cannot train a classifier in one space, and apply it to a different space.
(2) is cheating. When you train a classifier, you cannot use any information from the test set.
The correct way would be to run P... | PCA on train and test datasets: should I run one PCA on train+test or two separate on train and on t | (1) is incorrect, because if you run PCA on the two sets separately, you will end up with two different spaces. You cannot train a classifier in one space, and apply it to a different space.
(2) is ch | PCA on train and test datasets: should I run one PCA on train+test or two separate on train and on test? [duplicate]
(1) is incorrect, because if you run PCA on the two sets separately, you will end up with two different spaces. You cannot train a classifier in one space, and apply it to a different space.
(2) is cheat... | PCA on train and test datasets: should I run one PCA on train+test or two separate on train and on t
(1) is incorrect, because if you run PCA on the two sets separately, you will end up with two different spaces. You cannot train a classifier in one space, and apply it to a different space.
(2) is ch |
51,503 | Probability of each of the three Christmas puddings having exactly 2 coins | Call the number of pieces in each section $A$, $B$, and $C$. Because $A+B+C=6$, you are interested in $Pr(A=2, B=2) = Pr(B=2|A=2)Pr(A=2)$.
$Pr(A=2)$ is a simple binomial calculation: $A\sim Binom(6, 1/3)$, so $Pr(A=2) = {6\choose 2}(1/3)^2(2/3)^4 = 80/243$.
Conditioned on $A$ having two pieces, $B\sim Binom(4, 1/2)$, s... | Probability of each of the three Christmas puddings having exactly 2 coins | Call the number of pieces in each section $A$, $B$, and $C$. Because $A+B+C=6$, you are interested in $Pr(A=2, B=2) = Pr(B=2|A=2)Pr(A=2)$.
$Pr(A=2)$ is a simple binomial calculation: $A\sim Binom(6, 1 | Probability of each of the three Christmas puddings having exactly 2 coins
Call the number of pieces in each section $A$, $B$, and $C$. Because $A+B+C=6$, you are interested in $Pr(A=2, B=2) = Pr(B=2|A=2)Pr(A=2)$.
$Pr(A=2)$ is a simple binomial calculation: $A\sim Binom(6, 1/3)$, so $Pr(A=2) = {6\choose 2}(1/3)^2(2/3)^... | Probability of each of the three Christmas puddings having exactly 2 coins
Call the number of pieces in each section $A$, $B$, and $C$. Because $A+B+C=6$, you are interested in $Pr(A=2, B=2) = Pr(B=2|A=2)Pr(A=2)$.
$Pr(A=2)$ is a simple binomial calculation: $A\sim Binom(6, 1 |
51,504 | Probability of each of the three Christmas puddings having exactly 2 coins | You shouldn't use the binomial distribution here as it is a multinominal distribution problem (a generalization of the binomial).
So let's gather what we have:
n = 6 (total number of events)
n1 = 2 in part 1 (pudding #1)
n2 = 2 in part 2 (pudding #2)
n3 = 2 in part 3 (pudding #3)
p1 = 2/6 (probability to get 2 from n1... | Probability of each of the three Christmas puddings having exactly 2 coins | You shouldn't use the binomial distribution here as it is a multinominal distribution problem (a generalization of the binomial).
So let's gather what we have:
n = 6 (total number of events)
n1 = 2 i | Probability of each of the three Christmas puddings having exactly 2 coins
You shouldn't use the binomial distribution here as it is a multinominal distribution problem (a generalization of the binomial).
So let's gather what we have:
n = 6 (total number of events)
n1 = 2 in part 1 (pudding #1)
n2 = 2 in part 2 (puddi... | Probability of each of the three Christmas puddings having exactly 2 coins
You shouldn't use the binomial distribution here as it is a multinominal distribution problem (a generalization of the binomial).
So let's gather what we have:
n = 6 (total number of events)
n1 = 2 i |
51,505 | Statistical fallacy when not controlling for variables? | You could call it the omitted variable bias, (although that doesn't have "fallacy" in the name). It is a form of endogeneity; closely related to the omitted variable bias / another form of endogeneity is the ecological fallacy, which does have "fallacy" in the name.
For what it's worth, I'm not sure the statement as... | Statistical fallacy when not controlling for variables? | You could call it the omitted variable bias, (although that doesn't have "fallacy" in the name). It is a form of endogeneity; closely related to the omitted variable bias / another form of endogeneit | Statistical fallacy when not controlling for variables?
You could call it the omitted variable bias, (although that doesn't have "fallacy" in the name). It is a form of endogeneity; closely related to the omitted variable bias / another form of endogeneity is the ecological fallacy, which does have "fallacy" in the na... | Statistical fallacy when not controlling for variables?
You could call it the omitted variable bias, (although that doesn't have "fallacy" in the name). It is a form of endogeneity; closely related to the omitted variable bias / another form of endogeneit |
51,506 | Statistical fallacy when not controlling for variables? | There isn't a "fallacy" named after confounding, as far as I know. But if someone mistakenly suggested a causal relationship (like car brand and rusting), we called that a "spurious relationship." | Statistical fallacy when not controlling for variables? | There isn't a "fallacy" named after confounding, as far as I know. But if someone mistakenly suggested a causal relationship (like car brand and rusting), we called that a "spurious relationship." | Statistical fallacy when not controlling for variables?
There isn't a "fallacy" named after confounding, as far as I know. But if someone mistakenly suggested a causal relationship (like car brand and rusting), we called that a "spurious relationship." | Statistical fallacy when not controlling for variables?
There isn't a "fallacy" named after confounding, as far as I know. But if someone mistakenly suggested a causal relationship (like car brand and rusting), we called that a "spurious relationship." |
51,507 | Statistical fallacy when not controlling for variables? | This is not a statistical (associational) fallacy, this is a logical fallacy of a causal claim. Let's take the statement: "you never see a Ferrari rust like a Honda". Statistically this means that the observed rusting in the "population" of Ferraris is somehow different from the observed rusting in the "population" of ... | Statistical fallacy when not controlling for variables? | This is not a statistical (associational) fallacy, this is a logical fallacy of a causal claim. Let's take the statement: "you never see a Ferrari rust like a Honda". Statistically this means that the | Statistical fallacy when not controlling for variables?
This is not a statistical (associational) fallacy, this is a logical fallacy of a causal claim. Let's take the statement: "you never see a Ferrari rust like a Honda". Statistically this means that the observed rusting in the "population" of Ferraris is somehow dif... | Statistical fallacy when not controlling for variables?
This is not a statistical (associational) fallacy, this is a logical fallacy of a causal claim. Let's take the statement: "you never see a Ferrari rust like a Honda". Statistically this means that the |
51,508 | Statistical fallacy when not controlling for variables? | You may call it confounding or mediation depending on the exact relationship between the control variables and the variables of interest | Statistical fallacy when not controlling for variables? | You may call it confounding or mediation depending on the exact relationship between the control variables and the variables of interest | Statistical fallacy when not controlling for variables?
You may call it confounding or mediation depending on the exact relationship between the control variables and the variables of interest | Statistical fallacy when not controlling for variables?
You may call it confounding or mediation depending on the exact relationship between the control variables and the variables of interest |
51,509 | Statistical fallacy when not controlling for variables? | "Correlation does not imply Causation".
Clearly CarMake has a very strong CORRELATION with rust: CarMake=Honda often has rust, CarMake=Ferrari never has rust.
But that does not mean that CarMake CAUSES rust.
Instead, ConsumerDesireForLuxuryCar causes CarMake=Ferrari, and ConsumerDesireForLuxuryCar also cause TakingCare... | Statistical fallacy when not controlling for variables? | "Correlation does not imply Causation".
Clearly CarMake has a very strong CORRELATION with rust: CarMake=Honda often has rust, CarMake=Ferrari never has rust.
But that does not mean that CarMake CAUSE | Statistical fallacy when not controlling for variables?
"Correlation does not imply Causation".
Clearly CarMake has a very strong CORRELATION with rust: CarMake=Honda often has rust, CarMake=Ferrari never has rust.
But that does not mean that CarMake CAUSES rust.
Instead, ConsumerDesireForLuxuryCar causes CarMake=Ferra... | Statistical fallacy when not controlling for variables?
"Correlation does not imply Causation".
Clearly CarMake has a very strong CORRELATION with rust: CarMake=Honda often has rust, CarMake=Ferrari never has rust.
But that does not mean that CarMake CAUSE |
51,510 | Must we do feature selection in cross validation? | One of the most challenging elements of this problem is knowing when it's OK to put unsupervised learning steps outside of the CV loop and when they should be fully penalized for by including them inside the loop. Generally speaking, unsupervised learning procedures such as principal components analysis can be unstabl... | Must we do feature selection in cross validation? | One of the most challenging elements of this problem is knowing when it's OK to put unsupervised learning steps outside of the CV loop and when they should be fully penalized for by including them ins | Must we do feature selection in cross validation?
One of the most challenging elements of this problem is knowing when it's OK to put unsupervised learning steps outside of the CV loop and when they should be fully penalized for by including them inside the loop. Generally speaking, unsupervised learning procedures su... | Must we do feature selection in cross validation?
One of the most challenging elements of this problem is knowing when it's OK to put unsupervised learning steps outside of the CV loop and when they should be fully penalized for by including them ins |
51,511 | Must we do feature selection in cross validation? | Cross-validation is a means of estimating the performance of a method for fitting a model, rather than of the model itself, so all steps in fitting the model (including feature selection and optimising the hyper-parameters) need to be performed independently in each fold of the cross-validation procedure. If you don't... | Must we do feature selection in cross validation? | Cross-validation is a means of estimating the performance of a method for fitting a model, rather than of the model itself, so all steps in fitting the model (including feature selection and optimisin | Must we do feature selection in cross validation?
Cross-validation is a means of estimating the performance of a method for fitting a model, rather than of the model itself, so all steps in fitting the model (including feature selection and optimising the hyper-parameters) need to be performed independently in each fol... | Must we do feature selection in cross validation?
Cross-validation is a means of estimating the performance of a method for fitting a model, rather than of the model itself, so all steps in fitting the model (including feature selection and optimisin |
51,512 | Must we do feature selection in cross validation? | The thread you mentioned already discusses it in great detail, so I'd skip the parts that were already mentioned there. Answering your question, it depends on what you mean by "one shot feature selection before cv if we do not take response variable into account". For example, if you look at the data and discover that ... | Must we do feature selection in cross validation? | The thread you mentioned already discusses it in great detail, so I'd skip the parts that were already mentioned there. Answering your question, it depends on what you mean by "one shot feature select | Must we do feature selection in cross validation?
The thread you mentioned already discusses it in great detail, so I'd skip the parts that were already mentioned there. Answering your question, it depends on what you mean by "one shot feature selection before cv if we do not take response variable into account". For e... | Must we do feature selection in cross validation?
The thread you mentioned already discusses it in great detail, so I'd skip the parts that were already mentioned there. Answering your question, it depends on what you mean by "one shot feature select |
51,513 | Must we do feature selection in cross validation? | In principle, if you want your CV validation scores to reflect what applying an algorithm trained in the manner you did will do on new data, then everything should be part of the cross-validation (or bootstrapping, or whatever else you do along those lines). It's of course the most concerning when we need to make decis... | Must we do feature selection in cross validation? | In principle, if you want your CV validation scores to reflect what applying an algorithm trained in the manner you did will do on new data, then everything should be part of the cross-validation (or | Must we do feature selection in cross validation?
In principle, if you want your CV validation scores to reflect what applying an algorithm trained in the manner you did will do on new data, then everything should be part of the cross-validation (or bootstrapping, or whatever else you do along those lines). It's of cou... | Must we do feature selection in cross validation?
In principle, if you want your CV validation scores to reflect what applying an algorithm trained in the manner you did will do on new data, then everything should be part of the cross-validation (or |
51,514 | Exponential decay of ACF of AR(p) process | $f(h) =\phi^h$ is an exponential function. | Exponential decay of ACF of AR(p) process | $f(h) =\phi^h$ is an exponential function. | Exponential decay of ACF of AR(p) process
$f(h) =\phi^h$ is an exponential function. | Exponential decay of ACF of AR(p) process
$f(h) =\phi^h$ is an exponential function. |
51,515 | Exponential decay of ACF of AR(p) process | The magnitude of the ACF is an exponential function in $h$:
$$|\phi|^h = \exp( \log (|\phi|^h)) = \exp( h \log |\phi|).$$ | Exponential decay of ACF of AR(p) process | The magnitude of the ACF is an exponential function in $h$:
$$|\phi|^h = \exp( \log (|\phi|^h)) = \exp( h \log |\phi|).$$ | Exponential decay of ACF of AR(p) process
The magnitude of the ACF is an exponential function in $h$:
$$|\phi|^h = \exp( \log (|\phi|^h)) = \exp( h \log |\phi|).$$ | Exponential decay of ACF of AR(p) process
The magnitude of the ACF is an exponential function in $h$:
$$|\phi|^h = \exp( \log (|\phi|^h)) = \exp( h \log |\phi|).$$ |
51,516 | Exponential decay of ACF of AR(p) process | No, thinking in $h$, this is not power-law. It would be if it was something like $h^\phi$. Therefore, autocorrelation, $\phi^h$, is referred as exponential. | Exponential decay of ACF of AR(p) process | No, thinking in $h$, this is not power-law. It would be if it was something like $h^\phi$. Therefore, autocorrelation, $\phi^h$, is referred as exponential. | Exponential decay of ACF of AR(p) process
No, thinking in $h$, this is not power-law. It would be if it was something like $h^\phi$. Therefore, autocorrelation, $\phi^h$, is referred as exponential. | Exponential decay of ACF of AR(p) process
No, thinking in $h$, this is not power-law. It would be if it was something like $h^\phi$. Therefore, autocorrelation, $\phi^h$, is referred as exponential. |
51,517 | 'Size' of intercept at linear regression | The coefficients of each predictor are almost always going to change when you add more predictors. This is an example of the answer changing when you ask a different question.
Your software should let you fit a regression with no predictor at all. For example, if I try to predict people's weights with a regression with... | 'Size' of intercept at linear regression | The coefficients of each predictor are almost always going to change when you add more predictors. This is an example of the answer changing when you ask a different question.
Your software should let | 'Size' of intercept at linear regression
The coefficients of each predictor are almost always going to change when you add more predictors. This is an example of the answer changing when you ask a different question.
Your software should let you fit a regression with no predictor at all. For example, if I try to predic... | 'Size' of intercept at linear regression
The coefficients of each predictor are almost always going to change when you add more predictors. This is an example of the answer changing when you ask a different question.
Your software should let |
51,518 | 'Size' of intercept at linear regression | Nick Cox provided an excellent response and I wanted to add a more intuitive answer.
Model 1
Model 1 investigates the relationship between IQ and Brain size among subjects represented by the ones in the study, regardless of those subjects' Gender, Height and Weight.
In other words, if you imagine the target population ... | 'Size' of intercept at linear regression | Nick Cox provided an excellent response and I wanted to add a more intuitive answer.
Model 1
Model 1 investigates the relationship between IQ and Brain size among subjects represented by the ones in t | 'Size' of intercept at linear regression
Nick Cox provided an excellent response and I wanted to add a more intuitive answer.
Model 1
Model 1 investigates the relationship between IQ and Brain size among subjects represented by the ones in the study, regardless of those subjects' Gender, Height and Weight.
In other wor... | 'Size' of intercept at linear regression
Nick Cox provided an excellent response and I wanted to add a more intuitive answer.
Model 1
Model 1 investigates the relationship between IQ and Brain size among subjects represented by the ones in t |
51,519 | Can any dataset be clustered or does there need to be some sort of pattern in the data? | It seems to me there are two different primary goals one might have in clustering a dataset:
Identify latent groupings
Data reduction
Your question implies you have #1 in mind. As other answers have pointed out, determining if the clustering represents 'real' latent groups is a very difficult task. There are a la... | Can any dataset be clustered or does there need to be some sort of pattern in the data? | It seems to me there are two different primary goals one might have in clustering a dataset:
Identify latent groupings
Data reduction
Your question implies you have #1 in mind. As other answers h | Can any dataset be clustered or does there need to be some sort of pattern in the data?
It seems to me there are two different primary goals one might have in clustering a dataset:
Identify latent groupings
Data reduction
Your question implies you have #1 in mind. As other answers have pointed out, determining if ... | Can any dataset be clustered or does there need to be some sort of pattern in the data?
It seems to me there are two different primary goals one might have in clustering a dataset:
Identify latent groupings
Data reduction
Your question implies you have #1 in mind. As other answers h |
51,520 | Can any dataset be clustered or does there need to be some sort of pattern in the data? | Or is any set of data "clusterable"?
Yes, all data is clusterable -- even meaningless random data.
... how can we distinguish meaningful vs. non-meaningful clustering?
Depends on what you mean by "meaningful". Sometimes the clusters are useful, often they are not. You have to decide on a case-by-case basis.
Succes... | Can any dataset be clustered or does there need to be some sort of pattern in the data? | Or is any set of data "clusterable"?
Yes, all data is clusterable -- even meaningless random data.
... how can we distinguish meaningful vs. non-meaningful clustering?
Depends on what you mean by | Can any dataset be clustered or does there need to be some sort of pattern in the data?
Or is any set of data "clusterable"?
Yes, all data is clusterable -- even meaningless random data.
... how can we distinguish meaningful vs. non-meaningful clustering?
Depends on what you mean by "meaningful". Sometimes the clus... | Can any dataset be clustered or does there need to be some sort of pattern in the data?
Or is any set of data "clusterable"?
Yes, all data is clusterable -- even meaningless random data.
... how can we distinguish meaningful vs. non-meaningful clustering?
Depends on what you mean by |
51,521 | Can any dataset be clustered or does there need to be some sort of pattern in the data? | Both @Ray and @Anony-Mousse have captured the ambiguity in the question by highlighting that any data set can be fed into a clustering algorithms and that this does not imply that useful clusters will be found.
To address your question from a practical point of view, you can assess the clustering tendency of a given da... | Can any dataset be clustered or does there need to be some sort of pattern in the data? | Both @Ray and @Anony-Mousse have captured the ambiguity in the question by highlighting that any data set can be fed into a clustering algorithms and that this does not imply that useful clusters will | Can any dataset be clustered or does there need to be some sort of pattern in the data?
Both @Ray and @Anony-Mousse have captured the ambiguity in the question by highlighting that any data set can be fed into a clustering algorithms and that this does not imply that useful clusters will be found.
To address your quest... | Can any dataset be clustered or does there need to be some sort of pattern in the data?
Both @Ray and @Anony-Mousse have captured the ambiguity in the question by highlighting that any data set can be fed into a clustering algorithms and that this does not imply that useful clusters will |
51,522 | Can any dataset be clustered or does there need to be some sort of pattern in the data? | No. Not every data set is truly clustered. Sometimes you're lucky to deal with homogenous data, which is by definition not clustered. On the other hand, you can almost always find clusters in the data even if they're not there, think of fortune telling on tea leaves. | Can any dataset be clustered or does there need to be some sort of pattern in the data? | No. Not every data set is truly clustered. Sometimes you're lucky to deal with homogenous data, which is by definition not clustered. On the other hand, you can almost always find clusters in the data | Can any dataset be clustered or does there need to be some sort of pattern in the data?
No. Not every data set is truly clustered. Sometimes you're lucky to deal with homogenous data, which is by definition not clustered. On the other hand, you can almost always find clusters in the data even if they're not there, thin... | Can any dataset be clustered or does there need to be some sort of pattern in the data?
No. Not every data set is truly clustered. Sometimes you're lucky to deal with homogenous data, which is by definition not clustered. On the other hand, you can almost always find clusters in the data |
51,523 | Can any dataset be clustered or does there need to be some sort of pattern in the data? | Yes, any dataset can be clustered: label some points 0, some points 1 at random. Voila, it is clustered (useless, but clustered).
Even the "optimum" clustering of an algorithm frequently is useless. You cannot identify "usefulness" with some mathematical statistic on the input data. Instead, have a human user analyze t... | Can any dataset be clustered or does there need to be some sort of pattern in the data? | Yes, any dataset can be clustered: label some points 0, some points 1 at random. Voila, it is clustered (useless, but clustered).
Even the "optimum" clustering of an algorithm frequently is useless. Y | Can any dataset be clustered or does there need to be some sort of pattern in the data?
Yes, any dataset can be clustered: label some points 0, some points 1 at random. Voila, it is clustered (useless, but clustered).
Even the "optimum" clustering of an algorithm frequently is useless. You cannot identify "usefulness" ... | Can any dataset be clustered or does there need to be some sort of pattern in the data?
Yes, any dataset can be clustered: label some points 0, some points 1 at random. Voila, it is clustered (useless, but clustered).
Even the "optimum" clustering of an algorithm frequently is useless. Y |
51,524 | Why do we use separate priors or joint priors? | I think the correct way to phrase this is whether the priors are independent or not. The priors can always be described as (for example in your Normal example) $p(\mu, \sigma^2)$, but the question is does that joint prior factorize as $p(\mu, \sigma^2) = p(\mu)p(\sigma^2)$ or not.
Once we have that phrasing in place I... | Why do we use separate priors or joint priors? | I think the correct way to phrase this is whether the priors are independent or not. The priors can always be described as (for example in your Normal example) $p(\mu, \sigma^2)$, but the question is | Why do we use separate priors or joint priors?
I think the correct way to phrase this is whether the priors are independent or not. The priors can always be described as (for example in your Normal example) $p(\mu, \sigma^2)$, but the question is does that joint prior factorize as $p(\mu, \sigma^2) = p(\mu)p(\sigma^2)$... | Why do we use separate priors or joint priors?
I think the correct way to phrase this is whether the priors are independent or not. The priors can always be described as (for example in your Normal example) $p(\mu, \sigma^2)$, but the question is |
51,525 | Why do we use separate priors or joint priors? | All the priors you mention are "joint" priors in that they define a joint distribution on the parameter vector $\mathbf{\theta}=(\theta_1,\ldots,\theta_p)$. When the prior writes down as
$$\prod_{i=1}^p \pi_i(\theta_i)$$
each component $\pi_i(\theta_i)$ can also be interpreted as a (marginal) prior on the component $\t... | Why do we use separate priors or joint priors? | All the priors you mention are "joint" priors in that they define a joint distribution on the parameter vector $\mathbf{\theta}=(\theta_1,\ldots,\theta_p)$. When the prior writes down as
$$\prod_{i=1} | Why do we use separate priors or joint priors?
All the priors you mention are "joint" priors in that they define a joint distribution on the parameter vector $\mathbf{\theta}=(\theta_1,\ldots,\theta_p)$. When the prior writes down as
$$\prod_{i=1}^p \pi_i(\theta_i)$$
each component $\pi_i(\theta_i)$ can also be interpr... | Why do we use separate priors or joint priors?
All the priors you mention are "joint" priors in that they define a joint distribution on the parameter vector $\mathbf{\theta}=(\theta_1,\ldots,\theta_p)$. When the prior writes down as
$$\prod_{i=1} |
51,526 | Example of distribution whose support is strictly positive | You may be interested in this gallery of distributions. In addition to
the gamma distribution
the lognormal distribution
the $\chi^2$ distribution
and the truncated normal distribution
that have already been brought up, you could check
the F distribution
the exponential distribution
the Weibull distribution
the powe... | Example of distribution whose support is strictly positive | You may be interested in this gallery of distributions. In addition to
the gamma distribution
the lognormal distribution
the $\chi^2$ distribution
and the truncated normal distribution
that have alr | Example of distribution whose support is strictly positive
You may be interested in this gallery of distributions. In addition to
the gamma distribution
the lognormal distribution
the $\chi^2$ distribution
and the truncated normal distribution
that have already been brought up, you could check
the F distribution
the... | Example of distribution whose support is strictly positive
You may be interested in this gallery of distributions. In addition to
the gamma distribution
the lognormal distribution
the $\chi^2$ distribution
and the truncated normal distribution
that have alr |
51,527 | Example of distribution whose support is strictly positive | What about the truncated normal distribution? Try for example
library(truncnorm)
x <- seq(0,10,by=.01)
plot(x,dtruncnorm(x, a=3, b=Inf, mean = 5, sd = 1),type="l")
This gives
By taking the mean of the underlying normal distribution $\mu$ (mean in the command) larger you can make it look "almost" normal, without a non... | Example of distribution whose support is strictly positive | What about the truncated normal distribution? Try for example
library(truncnorm)
x <- seq(0,10,by=.01)
plot(x,dtruncnorm(x, a=3, b=Inf, mean = 5, sd = 1),type="l")
This gives
By taking the mean of t | Example of distribution whose support is strictly positive
What about the truncated normal distribution? Try for example
library(truncnorm)
x <- seq(0,10,by=.01)
plot(x,dtruncnorm(x, a=3, b=Inf, mean = 5, sd = 1),type="l")
This gives
By taking the mean of the underlying normal distribution $\mu$ (mean in the command)... | Example of distribution whose support is strictly positive
What about the truncated normal distribution? Try for example
library(truncnorm)
x <- seq(0,10,by=.01)
plot(x,dtruncnorm(x, a=3, b=Inf, mean = 5, sd = 1),type="l")
This gives
By taking the mean of t |
51,528 | Example of distribution whose support is strictly positive | There are infinitely many such distributions ...
Consider the family of uniform distributions from $0$ to $N$ (non-inclusive of $0$), where $N$ is an arbitrary integer.
Now choose any one of these, say $X_1 \sim U(0,3)$.
Then the sum of $X_1 + \dots + X_{10}$, where $X_1, \dots, X_{10} \overset{\text{iid}} \sim U(0,3... | Example of distribution whose support is strictly positive | There are infinitely many such distributions ...
Consider the family of uniform distributions from $0$ to $N$ (non-inclusive of $0$), where $N$ is an arbitrary integer.
Now choose any one of these, s | Example of distribution whose support is strictly positive
There are infinitely many such distributions ...
Consider the family of uniform distributions from $0$ to $N$ (non-inclusive of $0$), where $N$ is an arbitrary integer.
Now choose any one of these, say $X_1 \sim U(0,3)$.
Then the sum of $X_1 + \dots + X_{10}$... | Example of distribution whose support is strictly positive
There are infinitely many such distributions ...
Consider the family of uniform distributions from $0$ to $N$ (non-inclusive of $0$), where $N$ is an arbitrary integer.
Now choose any one of these, s |
51,529 | Example of distribution whose support is strictly positive | Two suggestions for you:
The Non-central $\chi^2_1$ distribution which is (can be) obtained by squaring the normal distribution $N(\mu,1)$?
This easily satisfies your relation to normal as it always shares the parameters of some underlying normal. However, it always skews right, so while it may look sort of normal... | Example of distribution whose support is strictly positive | Two suggestions for you:
The Non-central $\chi^2_1$ distribution which is (can be) obtained by squaring the normal distribution $N(\mu,1)$?
This easily satisfies your relation to normal as it alw | Example of distribution whose support is strictly positive
Two suggestions for you:
The Non-central $\chi^2_1$ distribution which is (can be) obtained by squaring the normal distribution $N(\mu,1)$?
This easily satisfies your relation to normal as it always shares the parameters of some underlying normal. However,... | Example of distribution whose support is strictly positive
Two suggestions for you:
The Non-central $\chi^2_1$ distribution which is (can be) obtained by squaring the normal distribution $N(\mu,1)$?
This easily satisfies your relation to normal as it alw |
51,530 | Is the key assumption for instrumental variables not testable? | The assumption is not correct as you stated it. The correct version is: the instrument I is independent of the outcome Y given the covariates X. This is called the exclusion restriction. If you ignore the covariates, then there should be a dependence of Y on I (otherwise either the link I -> X or the link X -> Y are mi... | Is the key assumption for instrumental variables not testable? | The assumption is not correct as you stated it. The correct version is: the instrument I is independent of the outcome Y given the covariates X. This is called the exclusion restriction. If you ignore | Is the key assumption for instrumental variables not testable?
The assumption is not correct as you stated it. The correct version is: the instrument I is independent of the outcome Y given the covariates X. This is called the exclusion restriction. If you ignore the covariates, then there should be a dependence of Y o... | Is the key assumption for instrumental variables not testable?
The assumption is not correct as you stated it. The correct version is: the instrument I is independent of the outcome Y given the covariates X. This is called the exclusion restriction. If you ignore |
51,531 | Is the key assumption for instrumental variables not testable? | In a regression like
$$Y_i = \alpha + \beta X_i + \eta_i$$
where $X_i$ is the endogenous variable such that $Cov(X_i,\eta_i)\neq 0$, a "good" instrument must satisfy two conditions, which are
$Cov(X_i,Z_i)\neq 0$, meaning that the instrument must be correlated with the endogenous variable, i.e. a first stage exists
$C... | Is the key assumption for instrumental variables not testable? | In a regression like
$$Y_i = \alpha + \beta X_i + \eta_i$$
where $X_i$ is the endogenous variable such that $Cov(X_i,\eta_i)\neq 0$, a "good" instrument must satisfy two conditions, which are
$Cov(X_ | Is the key assumption for instrumental variables not testable?
In a regression like
$$Y_i = \alpha + \beta X_i + \eta_i$$
where $X_i$ is the endogenous variable such that $Cov(X_i,\eta_i)\neq 0$, a "good" instrument must satisfy two conditions, which are
$Cov(X_i,Z_i)\neq 0$, meaning that the instrument must be correl... | Is the key assumption for instrumental variables not testable?
In a regression like
$$Y_i = \alpha + \beta X_i + \eta_i$$
where $X_i$ is the endogenous variable such that $Cov(X_i,\eta_i)\neq 0$, a "good" instrument must satisfy two conditions, which are
$Cov(X_ |
51,532 | Is the key assumption for instrumental variables not testable? | First, as others have said, the assumption as you have stated is not correct.
The standard IV model is given by,
And the key assumptions here are that $Z$ has no effect on $Y$ except through $X$ (exclusion restriction) and that there are no common causes between $Z$ and $Y$ (independence restriction, or unconfoundedn... | Is the key assumption for instrumental variables not testable? | First, as others have said, the assumption as you have stated is not correct.
The standard IV model is given by,
And the key assumptions here are that $Z$ has no effect on $Y$ except through $X$ (ex | Is the key assumption for instrumental variables not testable?
First, as others have said, the assumption as you have stated is not correct.
The standard IV model is given by,
And the key assumptions here are that $Z$ has no effect on $Y$ except through $X$ (exclusion restriction) and that there are no common causes ... | Is the key assumption for instrumental variables not testable?
First, as others have said, the assumption as you have stated is not correct.
The standard IV model is given by,
And the key assumptions here are that $Z$ has no effect on $Y$ except through $X$ (ex |
51,533 | Is the key assumption for instrumental variables not testable? | [I second Rob's clarification about revising the independence statement, but I disagree with his statements about testing the exclusion restriction.]
The exclusion restriction cannot be tested. Some tests are possible if the researcher imposes additional assumptions, but as a general rule the exclusion restriction cann... | Is the key assumption for instrumental variables not testable? | [I second Rob's clarification about revising the independence statement, but I disagree with his statements about testing the exclusion restriction.]
The exclusion restriction cannot be tested. Some t | Is the key assumption for instrumental variables not testable?
[I second Rob's clarification about revising the independence statement, but I disagree with his statements about testing the exclusion restriction.]
The exclusion restriction cannot be tested. Some tests are possible if the researcher imposes additional as... | Is the key assumption for instrumental variables not testable?
[I second Rob's clarification about revising the independence statement, but I disagree with his statements about testing the exclusion restriction.]
The exclusion restriction cannot be tested. Some t |
51,534 | Is the key assumption for instrumental variables not testable? | Several other answers have already done a good job explaining the underlying causal assumptions of the method (I especially like Carlos's answer). As has been pointed out, there are some observable implications to the satisfaction or non-satisfaction of the underlying instrumental assumption, and thus, it is not entir... | Is the key assumption for instrumental variables not testable? | Several other answers have already done a good job explaining the underlying causal assumptions of the method (I especially like Carlos's answer). As has been pointed out, there are some observable i | Is the key assumption for instrumental variables not testable?
Several other answers have already done a good job explaining the underlying causal assumptions of the method (I especially like Carlos's answer). As has been pointed out, there are some observable implications to the satisfaction or non-satisfaction of th... | Is the key assumption for instrumental variables not testable?
Several other answers have already done a good job explaining the underlying causal assumptions of the method (I especially like Carlos's answer). As has been pointed out, there are some observable i |
51,535 | Is the key assumption for instrumental variables not testable? | I don’t have the reputation to add a comment to Rob’s answer - the currently accepted answer.
However, describing the exclusion restriction as “Z is independent of Y given X” is not correct. Controlling for X induces spurious correlation between Z and Y as described in Carlos’s answer.
In the simple one variable toy ... | Is the key assumption for instrumental variables not testable? | I don’t have the reputation to add a comment to Rob’s answer - the currently accepted answer.
However, describing the exclusion restriction as “Z is independent of Y given X” is not correct. Controll | Is the key assumption for instrumental variables not testable?
I don’t have the reputation to add a comment to Rob’s answer - the currently accepted answer.
However, describing the exclusion restriction as “Z is independent of Y given X” is not correct. Controlling for X induces spurious correlation between Z and Y as... | Is the key assumption for instrumental variables not testable?
I don’t have the reputation to add a comment to Rob’s answer - the currently accepted answer.
However, describing the exclusion restriction as “Z is independent of Y given X” is not correct. Controll |
51,536 | In which case $\mathbb E[X]=\sum _ix_i P[x_i]$ can be $0$ when all $x$'s are not zero ($0$)? | How about
$$ x_1 = 1, P(x_1)=\frac{1}{2}, \qquad x_2 = -1, P(x_2)=\frac{1}{2} $$ | In which case $\mathbb E[X]=\sum _ix_i P[x_i]$ can be $0$ when all $x$'s are not zero ($0$)? | How about
$$ x_1 = 1, P(x_1)=\frac{1}{2}, \qquad x_2 = -1, P(x_2)=\frac{1}{2} $$ | In which case $\mathbb E[X]=\sum _ix_i P[x_i]$ can be $0$ when all $x$'s are not zero ($0$)?
How about
$$ x_1 = 1, P(x_1)=\frac{1}{2}, \qquad x_2 = -1, P(x_2)=\frac{1}{2} $$ | In which case $\mathbb E[X]=\sum _ix_i P[x_i]$ can be $0$ when all $x$'s are not zero ($0$)?
How about
$$ x_1 = 1, P(x_1)=\frac{1}{2}, \qquad x_2 = -1, P(x_2)=\frac{1}{2} $$ |
51,537 | In which case $\mathbb E[X]=\sum _ix_i P[x_i]$ can be $0$ when all $x$'s are not zero ($0$)? | At least one of the $x_i$ in the support of $X$ must be negative for the mean to be zero, so long as there is non-zero probability of a positive $x_i$ occurring. Otherwise each $x_i P(X=x_i) \geq 0$, and there is at least one $i$ for which $x_i P(X=x_i) > 0$, so $\mathbb{E}(X) = \sum_{i=1}^{n} x_i P(X=x_i) > 0$.
By ana... | In which case $\mathbb E[X]=\sum _ix_i P[x_i]$ can be $0$ when all $x$'s are not zero ($0$)? | At least one of the $x_i$ in the support of $X$ must be negative for the mean to be zero, so long as there is non-zero probability of a positive $x_i$ occurring. Otherwise each $x_i P(X=x_i) \geq 0$, | In which case $\mathbb E[X]=\sum _ix_i P[x_i]$ can be $0$ when all $x$'s are not zero ($0$)?
At least one of the $x_i$ in the support of $X$ must be negative for the mean to be zero, so long as there is non-zero probability of a positive $x_i$ occurring. Otherwise each $x_i P(X=x_i) \geq 0$, and there is at least one $... | In which case $\mathbb E[X]=\sum _ix_i P[x_i]$ can be $0$ when all $x$'s are not zero ($0$)?
At least one of the $x_i$ in the support of $X$ must be negative for the mean to be zero, so long as there is non-zero probability of a positive $x_i$ occurring. Otherwise each $x_i P(X=x_i) \geq 0$, |
51,538 | In which case $\mathbb E[X]=\sum _ix_i P[x_i]$ can be $0$ when all $x$'s are not zero ($0$)? | But I do not understand in which case $E[X]=∑_ix_iP[x_i]$ can be $0$ when all $x$'s are not zero ($0$)
You can construct any number of such distributions.
e.g. let $Y\sim F_Y$ for some distribution function $F_Y$ with mean $\mu$, in which not all values lie at the mean -- then some will lie above it and some will lie ... | In which case $\mathbb E[X]=\sum _ix_i P[x_i]$ can be $0$ when all $x$'s are not zero ($0$)? | But I do not understand in which case $E[X]=∑_ix_iP[x_i]$ can be $0$ when all $x$'s are not zero ($0$)
You can construct any number of such distributions.
e.g. let $Y\sim F_Y$ for some distribution f | In which case $\mathbb E[X]=\sum _ix_i P[x_i]$ can be $0$ when all $x$'s are not zero ($0$)?
But I do not understand in which case $E[X]=∑_ix_iP[x_i]$ can be $0$ when all $x$'s are not zero ($0$)
You can construct any number of such distributions.
e.g. let $Y\sim F_Y$ for some distribution function $F_Y$ with mean $\m... | In which case $\mathbb E[X]=\sum _ix_i P[x_i]$ can be $0$ when all $x$'s are not zero ($0$)?
But I do not understand in which case $E[X]=∑_ix_iP[x_i]$ can be $0$ when all $x$'s are not zero ($0$)
You can construct any number of such distributions.
e.g. let $Y\sim F_Y$ for some distribution f |
51,539 | When is the median more affected by sampling error than the mean? | Imagine that a variable takes values 0 and 1 with probability both 0.5. Sample from that distribution and most of the medians will be 0 or 1 and a very few exactly 0.5. The means will vary far less. The mean is much more stable in this circumstance.
Here is a sample graph of results. The plots are quantile plots, i.e.... | When is the median more affected by sampling error than the mean? | Imagine that a variable takes values 0 and 1 with probability both 0.5. Sample from that distribution and most of the medians will be 0 or 1 and a very few exactly 0.5. The means will vary far less. T | When is the median more affected by sampling error than the mean?
Imagine that a variable takes values 0 and 1 with probability both 0.5. Sample from that distribution and most of the medians will be 0 or 1 and a very few exactly 0.5. The means will vary far less. The mean is much more stable in this circumstance.
Her... | When is the median more affected by sampling error than the mean?
Imagine that a variable takes values 0 and 1 with probability both 0.5. Sample from that distribution and most of the medians will be 0 or 1 and a very few exactly 0.5. The means will vary far less. T |
51,540 | When is the median more affected by sampling error than the mean? | Where did you hear this? The usual reason for preferring the median is that it is less affected by extreme values than the mean. However, it is in general less sensitive to changes in the data.
I ran a tiny example in R
set.seed(1234)
true <- rnorm(1000)
smallerror <- true + rnorm(1000,0,.1)
largeerror <- true + rnor... | When is the median more affected by sampling error than the mean? | Where did you hear this? The usual reason for preferring the median is that it is less affected by extreme values than the mean. However, it is in general less sensitive to changes in the data.
I ran | When is the median more affected by sampling error than the mean?
Where did you hear this? The usual reason for preferring the median is that it is less affected by extreme values than the mean. However, it is in general less sensitive to changes in the data.
I ran a tiny example in R
set.seed(1234)
true <- rnorm(100... | When is the median more affected by sampling error than the mean?
Where did you hear this? The usual reason for preferring the median is that it is less affected by extreme values than the mean. However, it is in general less sensitive to changes in the data.
I ran |
51,541 | Wolfram Mathematica, MATLAB or something else? | Wolfram Mathematica is a very capable software for doing statistics, and unlike Matlab, its statistical functionality is included in the core Mathematica.
Unlike R or Matlab it provides symbolic support for probability computations. You can peruse Probability & Statistics guide page to get an idea about the functiona... | Wolfram Mathematica, MATLAB or something else? | Wolfram Mathematica is a very capable software for doing statistics, and unlike Matlab, its statistical functionality is included in the core Mathematica.
Unlike R or Matlab it provides symbolic sup | Wolfram Mathematica, MATLAB or something else?
Wolfram Mathematica is a very capable software for doing statistics, and unlike Matlab, its statistical functionality is included in the core Mathematica.
Unlike R or Matlab it provides symbolic support for probability computations. You can peruse Probability & Statistic... | Wolfram Mathematica, MATLAB or something else?
Wolfram Mathematica is a very capable software for doing statistics, and unlike Matlab, its statistical functionality is included in the core Mathematica.
Unlike R or Matlab it provides symbolic sup |
51,542 | Wolfram Mathematica, MATLAB or something else? | For statistics I would recommend R - computation environment concerning statistics.
Here you can find many tutorials for R. | Wolfram Mathematica, MATLAB or something else? | For statistics I would recommend R - computation environment concerning statistics.
Here you can find many tutorials for R. | Wolfram Mathematica, MATLAB or something else?
For statistics I would recommend R - computation environment concerning statistics.
Here you can find many tutorials for R. | Wolfram Mathematica, MATLAB or something else?
For statistics I would recommend R - computation environment concerning statistics.
Here you can find many tutorials for R. |
51,543 | Wolfram Mathematica, MATLAB or something else? | There are specific tools out there for statistical analysis. For example, R, SPSS, and Minitab.
Nevertheless, both Mathematica and MATLAB are capable of doing the required computations; which is "better" is a matter of taste and application requirements, most of the time.
MATLAB doesn't ship default with many statistic... | Wolfram Mathematica, MATLAB or something else? | There are specific tools out there for statistical analysis. For example, R, SPSS, and Minitab.
Nevertheless, both Mathematica and MATLAB are capable of doing the required computations; which is "bett | Wolfram Mathematica, MATLAB or something else?
There are specific tools out there for statistical analysis. For example, R, SPSS, and Minitab.
Nevertheless, both Mathematica and MATLAB are capable of doing the required computations; which is "better" is a matter of taste and application requirements, most of the time.
... | Wolfram Mathematica, MATLAB or something else?
There are specific tools out there for statistical analysis. For example, R, SPSS, and Minitab.
Nevertheless, both Mathematica and MATLAB are capable of doing the required computations; which is "bett |
51,544 | Wolfram Mathematica, MATLAB or something else? | Some people would say that Matlab is for numerical matrix calculations and simulations, and Mathematica for symbolic calculation. But nowadays both can perform both things. And we should add another contender, Maple.
In my opinion Matlab is too big and needs too much resources. You should only choose it if your compan... | Wolfram Mathematica, MATLAB or something else? | Some people would say that Matlab is for numerical matrix calculations and simulations, and Mathematica for symbolic calculation. But nowadays both can perform both things. And we should add another | Wolfram Mathematica, MATLAB or something else?
Some people would say that Matlab is for numerical matrix calculations and simulations, and Mathematica for symbolic calculation. But nowadays both can perform both things. And we should add another contender, Maple.
In my opinion Matlab is too big and needs too much reso... | Wolfram Mathematica, MATLAB or something else?
Some people would say that Matlab is for numerical matrix calculations and simulations, and Mathematica for symbolic calculation. But nowadays both can perform both things. And we should add another |
51,545 | Logistic regression with LBFGS solver | Here is an example of logistic regression estimation using the limited memory BFGS [L-BFGS]
optimization algorithm. I will be using the optimx function from the optimx library in R, and SciPy's
scipy.optimize.fmin_l_bfgs_b in Python.
Python
The example that I am using is from Sheather (2009, pg. 264). The following Py... | Logistic regression with LBFGS solver | Here is an example of logistic regression estimation using the limited memory BFGS [L-BFGS]
optimization algorithm. I will be using the optimx function from the optimx library in R, and SciPy's
scipy | Logistic regression with LBFGS solver
Here is an example of logistic regression estimation using the limited memory BFGS [L-BFGS]
optimization algorithm. I will be using the optimx function from the optimx library in R, and SciPy's
scipy.optimize.fmin_l_bfgs_b in Python.
Python
The example that I am using is from Shea... | Logistic regression with LBFGS solver
Here is an example of logistic regression estimation using the limited memory BFGS [L-BFGS]
optimization algorithm. I will be using the optimx function from the optimx library in R, and SciPy's
scipy |
51,546 | Logistic regression with LBFGS solver | If you're worrying about memory I guess you're either working with embedded hardware or expecting to have a big model. I'm going to guess that it's the latter and that you have a high dimensional text or bioinformatics classification problem of some sort. If so you should ponder Mallet's Java implementation, since th... | Logistic regression with LBFGS solver | If you're worrying about memory I guess you're either working with embedded hardware or expecting to have a big model. I'm going to guess that it's the latter and that you have a high dimensional tex | Logistic regression with LBFGS solver
If you're worrying about memory I guess you're either working with embedded hardware or expecting to have a big model. I'm going to guess that it's the latter and that you have a high dimensional text or bioinformatics classification problem of some sort. If so you should ponder ... | Logistic regression with LBFGS solver
If you're worrying about memory I guess you're either working with embedded hardware or expecting to have a big model. I'm going to guess that it's the latter and that you have a high dimensional tex |
51,547 | Logistic regression with LBFGS solver | The Apache Spark compute engine is open source and has great performance on very large datasets. As of version 1.2 (I think) from 2014, Spark MLlib supports LogisticRegressionWithLBFGS. The API has bindings for Python, Scala or Java.
It uses feature scaling and L2-Regularization by default, unlike the gsm method in R... | Logistic regression with LBFGS solver | The Apache Spark compute engine is open source and has great performance on very large datasets. As of version 1.2 (I think) from 2014, Spark MLlib supports LogisticRegressionWithLBFGS. The API has | Logistic regression with LBFGS solver
The Apache Spark compute engine is open source and has great performance on very large datasets. As of version 1.2 (I think) from 2014, Spark MLlib supports LogisticRegressionWithLBFGS. The API has bindings for Python, Scala or Java.
It uses feature scaling and L2-Regularization ... | Logistic regression with LBFGS solver
The Apache Spark compute engine is open source and has great performance on very large datasets. As of version 1.2 (I think) from 2014, Spark MLlib supports LogisticRegressionWithLBFGS. The API has |
51,548 | Logistic regression with LBFGS solver | Sk-learn has an excellent Logistic Regression implementation. It's just a wrapper around [LIBLINEAR], but LIBLINEAR is state-of-the-art and although it doesn't use LBFGS, it uses something else called dual-coordinate descent, which according to this paper is even better in many situations.
An alternate supposedly Pyth... | Logistic regression with LBFGS solver | Sk-learn has an excellent Logistic Regression implementation. It's just a wrapper around [LIBLINEAR], but LIBLINEAR is state-of-the-art and although it doesn't use LBFGS, it uses something else called | Logistic regression with LBFGS solver
Sk-learn has an excellent Logistic Regression implementation. It's just a wrapper around [LIBLINEAR], but LIBLINEAR is state-of-the-art and although it doesn't use LBFGS, it uses something else called dual-coordinate descent, which according to this paper is even better in many sit... | Logistic regression with LBFGS solver
Sk-learn has an excellent Logistic Regression implementation. It's just a wrapper around [LIBLINEAR], but LIBLINEAR is state-of-the-art and although it doesn't use LBFGS, it uses something else called |
51,549 | Logistic regression with LBFGS solver | From here, http://www.kazanovaforanalytics.com/download.html, You can download a .jar with an implementation of logistic regression via newton raphson method that minimizes the -2 log likelihood. A comprehensive example can be found here :
http://www.kazanovaforanalytics.com/example_classes.txt
Provided via Apache lic... | Logistic regression with LBFGS solver | From here, http://www.kazanovaforanalytics.com/download.html, You can download a .jar with an implementation of logistic regression via newton raphson method that minimizes the -2 log likelihood. A c | Logistic regression with LBFGS solver
From here, http://www.kazanovaforanalytics.com/download.html, You can download a .jar with an implementation of logistic regression via newton raphson method that minimizes the -2 log likelihood. A comprehensive example can be found here :
http://www.kazanovaforanalytics.com/examp... | Logistic regression with LBFGS solver
From here, http://www.kazanovaforanalytics.com/download.html, You can download a .jar with an implementation of logistic regression via newton raphson method that minimizes the -2 log likelihood. A c |
51,550 | How can I integrate R with PHP? [closed] | Here is the easiest way to do it that I found:
This implementation of PHP and R consists of only two files. One written in PHP, and the other an R script. The PHP returns a form which uses the GET method to send a variable N to the server. When the form is submitted, the PHP will then execute an R script from the shell... | How can I integrate R with PHP? [closed] | Here is the easiest way to do it that I found:
This implementation of PHP and R consists of only two files. One written in PHP, and the other an R script. The PHP returns a form which uses the GET met | How can I integrate R with PHP? [closed]
Here is the easiest way to do it that I found:
This implementation of PHP and R consists of only two files. One written in PHP, and the other an R script. The PHP returns a form which uses the GET method to send a variable N to the server. When the form is submitted, the PHP wil... | How can I integrate R with PHP? [closed]
Here is the easiest way to do it that I found:
This implementation of PHP and R consists of only two files. One written in PHP, and the other an R script. The PHP returns a form which uses the GET met |
51,551 | How can I integrate R with PHP? [closed] | If you ever think to switch to Linux, the best way would be to use RApache, which is an Apache module that embeds an R interpreter (mod_R) in the webserver | How can I integrate R with PHP? [closed] | If you ever think to switch to Linux, the best way would be to use RApache, which is an Apache module that embeds an R interpreter (mod_R) in the webserver | How can I integrate R with PHP? [closed]
If you ever think to switch to Linux, the best way would be to use RApache, which is an Apache module that embeds an R interpreter (mod_R) in the webserver | How can I integrate R with PHP? [closed]
If you ever think to switch to Linux, the best way would be to use RApache, which is an Apache module that embeds an R interpreter (mod_R) in the webserver |
51,552 | How can I integrate R with PHP? [closed] | If you are looking for a way of executing chunks of R code from PHP, here is a library that might help:
https://github.com/kachkaev/php-r
use Kachkaev\PHPR\RCore;
use Kachkaev\PHPR\Engine\CommandLineREngine;
$r = new RCore(new CommandLineREngine('/usr/bin/R'));
$result = $r->run('1 + 1');
echo $result;
This will outp... | How can I integrate R with PHP? [closed] | If you are looking for a way of executing chunks of R code from PHP, here is a library that might help:
https://github.com/kachkaev/php-r
use Kachkaev\PHPR\RCore;
use Kachkaev\PHPR\Engine\CommandLineR | How can I integrate R with PHP? [closed]
If you are looking for a way of executing chunks of R code from PHP, here is a library that might help:
https://github.com/kachkaev/php-r
use Kachkaev\PHPR\RCore;
use Kachkaev\PHPR\Engine\CommandLineREngine;
$r = new RCore(new CommandLineREngine('/usr/bin/R'));
$result = $r->ru... | How can I integrate R with PHP? [closed]
If you are looking for a way of executing chunks of R code from PHP, here is a library that might help:
https://github.com/kachkaev/php-r
use Kachkaev\PHPR\RCore;
use Kachkaev\PHPR\Engine\CommandLineR |
51,553 | Is Spearman's correlation coefficient usable to compare distributions? | For measuring the bin frequencies of two distributions, a pretty good test is the Chi Square test. It is exactly what it is designed for. And, it is even nonparametric. The distribution don't even have to be normal or symmetric. It is much better than the Kolmogorov-Smirnov test that is known to be weak in fitting ... | Is Spearman's correlation coefficient usable to compare distributions? | For measuring the bin frequencies of two distributions, a pretty good test is the Chi Square test. It is exactly what it is designed for. And, it is even nonparametric. The distribution don't even | Is Spearman's correlation coefficient usable to compare distributions?
For measuring the bin frequencies of two distributions, a pretty good test is the Chi Square test. It is exactly what it is designed for. And, it is even nonparametric. The distribution don't even have to be normal or symmetric. It is much bette... | Is Spearman's correlation coefficient usable to compare distributions?
For measuring the bin frequencies of two distributions, a pretty good test is the Chi Square test. It is exactly what it is designed for. And, it is even nonparametric. The distribution don't even |
51,554 | Is Spearman's correlation coefficient usable to compare distributions? | Rather use Kolmogorov–Smirnov test, which is exactly what you need. R function ks.test implements it.
Also check this question. | Is Spearman's correlation coefficient usable to compare distributions? | Rather use Kolmogorov–Smirnov test, which is exactly what you need. R function ks.test implements it.
Also check this question. | Is Spearman's correlation coefficient usable to compare distributions?
Rather use Kolmogorov–Smirnov test, which is exactly what you need. R function ks.test implements it.
Also check this question. | Is Spearman's correlation coefficient usable to compare distributions?
Rather use Kolmogorov–Smirnov test, which is exactly what you need. R function ks.test implements it.
Also check this question. |
51,555 | Is Spearman's correlation coefficient usable to compare distributions? | The Baumgartner-Weiss-Schindler statistic is a modern alternative to the K-S test, and appears to be more powerful in certain situations. A few links:
A Nonparametric Test for the General Two-Sample Problem (the original B.W.S. paper)
M. Neuhauser, 'Exact Tests Based on the Baumgartner-Weiss-Schindler Statistic--A Sur... | Is Spearman's correlation coefficient usable to compare distributions? | The Baumgartner-Weiss-Schindler statistic is a modern alternative to the K-S test, and appears to be more powerful in certain situations. A few links:
A Nonparametric Test for the General Two-Sample | Is Spearman's correlation coefficient usable to compare distributions?
The Baumgartner-Weiss-Schindler statistic is a modern alternative to the K-S test, and appears to be more powerful in certain situations. A few links:
A Nonparametric Test for the General Two-Sample Problem (the original B.W.S. paper)
M. Neuhauser,... | Is Spearman's correlation coefficient usable to compare distributions?
The Baumgartner-Weiss-Schindler statistic is a modern alternative to the K-S test, and appears to be more powerful in certain situations. A few links:
A Nonparametric Test for the General Two-Sample |
51,556 | How does an ideal prior distribution needs a probability mass on zero to reduce variance, and have fat tails to reduce bias? | The MAP estimator can have non-zero probability mass at a point (even if the posterior distribution is always continuous)
The linked article is actually a bit misleading on this point, since even under the stipulated model all the relevant distributions are still continuous, so there is still zero probability mass at t... | How does an ideal prior distribution needs a probability mass on zero to reduce variance, and have f | The MAP estimator can have non-zero probability mass at a point (even if the posterior distribution is always continuous)
The linked article is actually a bit misleading on this point, since even unde | How does an ideal prior distribution needs a probability mass on zero to reduce variance, and have fat tails to reduce bias?
The MAP estimator can have non-zero probability mass at a point (even if the posterior distribution is always continuous)
The linked article is actually a bit misleading on this point, since even... | How does an ideal prior distribution needs a probability mass on zero to reduce variance, and have f
The MAP estimator can have non-zero probability mass at a point (even if the posterior distribution is always continuous)
The linked article is actually a bit misleading on this point, since even unde |
51,557 | How does an ideal prior distribution needs a probability mass on zero to reduce variance, and have fat tails to reduce bias? | The idea is that you want your regularisation procedure to set small parameter estimates to zero and leave large estimates unchanged.
Now, lasso does zero out small estimates (ridge doesn't even do that), but both lasso and ridge shrink large estimates towards zero, which is a significant source of bias in the two proc... | How does an ideal prior distribution needs a probability mass on zero to reduce variance, and have f | The idea is that you want your regularisation procedure to set small parameter estimates to zero and leave large estimates unchanged.
Now, lasso does zero out small estimates (ridge doesn't even do th | How does an ideal prior distribution needs a probability mass on zero to reduce variance, and have fat tails to reduce bias?
The idea is that you want your regularisation procedure to set small parameter estimates to zero and leave large estimates unchanged.
Now, lasso does zero out small estimates (ridge doesn't even ... | How does an ideal prior distribution needs a probability mass on zero to reduce variance, and have f
The idea is that you want your regularisation procedure to set small parameter estimates to zero and leave large estimates unchanged.
Now, lasso does zero out small estimates (ridge doesn't even do th |
51,558 | How does an ideal prior distribution needs a probability mass on zero to reduce variance, and have fat tails to reduce bias? | Probability mass at zero
How does the normal distribution have a zero probability mass at zero?
The normal distribution has a non zero density at zero but the probability (mass) is zero $P[X=0] = 0$.
By placing a probability mass at zero the prior is expressing more strongly the believe that a parameter is probably z... | How does an ideal prior distribution needs a probability mass on zero to reduce variance, and have f | Probability mass at zero
How does the normal distribution have a zero probability mass at zero?
The normal distribution has a non zero density at zero but the probability (mass) is zero $P[X=0] = 0$ | How does an ideal prior distribution needs a probability mass on zero to reduce variance, and have fat tails to reduce bias?
Probability mass at zero
How does the normal distribution have a zero probability mass at zero?
The normal distribution has a non zero density at zero but the probability (mass) is zero $P[X=0]... | How does an ideal prior distribution needs a probability mass on zero to reduce variance, and have f
Probability mass at zero
How does the normal distribution have a zero probability mass at zero?
The normal distribution has a non zero density at zero but the probability (mass) is zero $P[X=0] = 0$ |
51,559 | Should the t-statistic (not data) be normally distributed for using the t-test? | Quoting (the complete sentence) from Wikipedia
It is most commonly applied when the test statistic would follow a
normal distribution if the value of a scaling term in the test
statistic were known (typically, the scaling term is unknown and
therefore a nuisance parameter).
This sentence is true. Indeed, as stated in... | Should the t-statistic (not data) be normally distributed for using the t-test? | Quoting (the complete sentence) from Wikipedia
It is most commonly applied when the test statistic would follow a
normal distribution if the value of a scaling term in the test
statistic were known ( | Should the t-statistic (not data) be normally distributed for using the t-test?
Quoting (the complete sentence) from Wikipedia
It is most commonly applied when the test statistic would follow a
normal distribution if the value of a scaling term in the test
statistic were known (typically, the scaling term is unknown a... | Should the t-statistic (not data) be normally distributed for using the t-test?
Quoting (the complete sentence) from Wikipedia
It is most commonly applied when the test statistic would follow a
normal distribution if the value of a scaling term in the test
statistic were known ( |
51,560 | Should the t-statistic (not data) be normally distributed for using the t-test? | The $t$-statistic should be $t$- distributed.
This is guaranteed by math when the data are $iid$ normal, which is the legendary normality assumption. However, the t-stats often have close to the correct distribution, especially in large sample sizes, even when the data violate the normality assumption. That is, the usu... | Should the t-statistic (not data) be normally distributed for using the t-test? | The $t$-statistic should be $t$- distributed.
This is guaranteed by math when the data are $iid$ normal, which is the legendary normality assumption. However, the t-stats often have close to the corre | Should the t-statistic (not data) be normally distributed for using the t-test?
The $t$-statistic should be $t$- distributed.
This is guaranteed by math when the data are $iid$ normal, which is the legendary normality assumption. However, the t-stats often have close to the correct distribution, especially in large sam... | Should the t-statistic (not data) be normally distributed for using the t-test?
The $t$-statistic should be $t$- distributed.
This is guaranteed by math when the data are $iid$ normal, which is the legendary normality assumption. However, the t-stats often have close to the corre |
51,561 | Should the t-statistic (not data) be normally distributed for using the t-test? | The full statement from Wikipedia is necessary to establish the proper context (emphasis mine):
A t-test is any statistical hypothesis test in which the test statistic follows a Student's t-distribution under the null hypothesis. It is most commonly applied when the test statistic would follow a normal distribution if... | Should the t-statistic (not data) be normally distributed for using the t-test? | The full statement from Wikipedia is necessary to establish the proper context (emphasis mine):
A t-test is any statistical hypothesis test in which the test statistic follows a Student's t-distribut | Should the t-statistic (not data) be normally distributed for using the t-test?
The full statement from Wikipedia is necessary to establish the proper context (emphasis mine):
A t-test is any statistical hypothesis test in which the test statistic follows a Student's t-distribution under the null hypothesis. It is mos... | Should the t-statistic (not data) be normally distributed for using the t-test?
The full statement from Wikipedia is necessary to establish the proper context (emphasis mine):
A t-test is any statistical hypothesis test in which the test statistic follows a Student's t-distribut |
51,562 | Chi squared test with reasonable sample size results in R warning | This warning is due to an error by Pearson, the inventor of the test, who wrongly estimated that P-values would not be accurate were an expected cell frequency be less than 5. See this. | Chi squared test with reasonable sample size results in R warning | This warning is due to an error by Pearson, the inventor of the test, who wrongly estimated that P-values would not be accurate were an expected cell frequency be less than 5. See this. | Chi squared test with reasonable sample size results in R warning
This warning is due to an error by Pearson, the inventor of the test, who wrongly estimated that P-values would not be accurate were an expected cell frequency be less than 5. See this. | Chi squared test with reasonable sample size results in R warning
This warning is due to an error by Pearson, the inventor of the test, who wrongly estimated that P-values would not be accurate were an expected cell frequency be less than 5. See this. |
51,563 | Chi squared test with reasonable sample size results in R warning | As a supplement to the answer by @Frank Harrell, here are the observed and expected frequencies and so-called Pearson residuals, (observed $-$ expected) / sqrt(expected). The name of the latter is generous to Pearson, but honours the fact that the chi-square statistic can be regarded as the sum of such residuals square... | Chi squared test with reasonable sample size results in R warning | As a supplement to the answer by @Frank Harrell, here are the observed and expected frequencies and so-called Pearson residuals, (observed $-$ expected) / sqrt(expected). The name of the latter is gen | Chi squared test with reasonable sample size results in R warning
As a supplement to the answer by @Frank Harrell, here are the observed and expected frequencies and so-called Pearson residuals, (observed $-$ expected) / sqrt(expected). The name of the latter is generous to Pearson, but honours the fact that the chi-sq... | Chi squared test with reasonable sample size results in R warning
As a supplement to the answer by @Frank Harrell, here are the observed and expected frequencies and so-called Pearson residuals, (observed $-$ expected) / sqrt(expected). The name of the latter is gen |
51,564 | How do you learn labels with unsupervised learning? | Normally, you don't (and you don't believe everything someone writes somewhere on the internet).
What the writer probably meant (at least that's my interpretation) is that you can use clustering to identify the clusters, declare each cluster to be a class for itself, and use these "classes" to learn class boundaries or... | How do you learn labels with unsupervised learning? | Normally, you don't (and you don't believe everything someone writes somewhere on the internet).
What the writer probably meant (at least that's my interpretation) is that you can use clustering to id | How do you learn labels with unsupervised learning?
Normally, you don't (and you don't believe everything someone writes somewhere on the internet).
What the writer probably meant (at least that's my interpretation) is that you can use clustering to identify the clusters, declare each cluster to be a class for itself, ... | How do you learn labels with unsupervised learning?
Normally, you don't (and you don't believe everything someone writes somewhere on the internet).
What the writer probably meant (at least that's my interpretation) is that you can use clustering to id |
51,565 | How do you learn labels with unsupervised learning? | This pops up a lot when labelling your full data set is expensive and time consuming. A simple example would be labelling product reviews into buckets such as:
Price Related
Shipping Related
Quality Related
What we may do is label a small fraction of our dataset and then we can cluster the word vecs, do knn with them... | How do you learn labels with unsupervised learning? | This pops up a lot when labelling your full data set is expensive and time consuming. A simple example would be labelling product reviews into buckets such as:
Price Related
Shipping Related
Quality | How do you learn labels with unsupervised learning?
This pops up a lot when labelling your full data set is expensive and time consuming. A simple example would be labelling product reviews into buckets such as:
Price Related
Shipping Related
Quality Related
What we may do is label a small fraction of our dataset and... | How do you learn labels with unsupervised learning?
This pops up a lot when labelling your full data set is expensive and time consuming. A simple example would be labelling product reviews into buckets such as:
Price Related
Shipping Related
Quality |
51,566 | How do you learn labels with unsupervised learning? | Unsupervised methods usually assign data points to clusters, which could be considered algorithmically generated labels. We don't "learn" labels in the sense that there is some true target label we want to identify, but rather create labels and assign them to the data. An unsupervised clustering will identify natural g... | How do you learn labels with unsupervised learning? | Unsupervised methods usually assign data points to clusters, which could be considered algorithmically generated labels. We don't "learn" labels in the sense that there is some true target label we wa | How do you learn labels with unsupervised learning?
Unsupervised methods usually assign data points to clusters, which could be considered algorithmically generated labels. We don't "learn" labels in the sense that there is some true target label we want to identify, but rather create labels and assign them to the data... | How do you learn labels with unsupervised learning?
Unsupervised methods usually assign data points to clusters, which could be considered algorithmically generated labels. We don't "learn" labels in the sense that there is some true target label we wa |
51,567 | Uniform posterior on bounded space vs unbounded space | It is not possible to have a flat (uniform) probability distribution on an unbounded space, so in particular it's not possible to have a flat posterior distribution.
If you had a uniform probability density on the entire real line, you would need a function $f(x)$ that integrated to 1 (to be a probability density) but ... | Uniform posterior on bounded space vs unbounded space | It is not possible to have a flat (uniform) probability distribution on an unbounded space, so in particular it's not possible to have a flat posterior distribution.
If you had a uniform probability d | Uniform posterior on bounded space vs unbounded space
It is not possible to have a flat (uniform) probability distribution on an unbounded space, so in particular it's not possible to have a flat posterior distribution.
If you had a uniform probability density on the entire real line, you would need a function $f(x)$ t... | Uniform posterior on bounded space vs unbounded space
It is not possible to have a flat (uniform) probability distribution on an unbounded space, so in particular it's not possible to have a flat posterior distribution.
If you had a uniform probability d |
51,568 | Uniform posterior on bounded space vs unbounded space | Strictly speaking, the question is imprecise in that it does not specify the reference measure. If the reference measure is $\text{d}\mu(x)=e^{-x^2}\text{d}\lambda(x)$ where $\lambda$ is the Lebesgue measure, a posterior with a flat density is valid.
Assuming however using a "flat prior" means having a constant density... | Uniform posterior on bounded space vs unbounded space | Strictly speaking, the question is imprecise in that it does not specify the reference measure. If the reference measure is $\text{d}\mu(x)=e^{-x^2}\text{d}\lambda(x)$ where $\lambda$ is the Lebesgue | Uniform posterior on bounded space vs unbounded space
Strictly speaking, the question is imprecise in that it does not specify the reference measure. If the reference measure is $\text{d}\mu(x)=e^{-x^2}\text{d}\lambda(x)$ where $\lambda$ is the Lebesgue measure, a posterior with a flat density is valid.
Assuming howeve... | Uniform posterior on bounded space vs unbounded space
Strictly speaking, the question is imprecise in that it does not specify the reference measure. If the reference measure is $\text{d}\mu(x)=e^{-x^2}\text{d}\lambda(x)$ where $\lambda$ is the Lebesgue |
51,569 | Is there any non-gaussian distribution has skewness 0 and kurtosis 3? | The discrete distribution with probabilities
\begin{align}
p(-2)&=1/12\\
p(-1)&=\ 1/6\\
p(0)&=\ 1/2\\
p(1)&= \ 1/6\\
p(2)&=1/12
\end{align}
has the same mean, variance, skewness and kurtosis as the Gaussian. | Is there any non-gaussian distribution has skewness 0 and kurtosis 3? | The discrete distribution with probabilities
\begin{align}
p(-2)&=1/12\\
p(-1)&=\ 1/6\\
p(0)&=\ 1/2\\
p(1)&= \ 1/6\\
p(2)&=1/12
\end{align}
has the same mean, variance, skewness and kurtosis as the Ga | Is there any non-gaussian distribution has skewness 0 and kurtosis 3?
The discrete distribution with probabilities
\begin{align}
p(-2)&=1/12\\
p(-1)&=\ 1/6\\
p(0)&=\ 1/2\\
p(1)&= \ 1/6\\
p(2)&=1/12
\end{align}
has the same mean, variance, skewness and kurtosis as the Gaussian. | Is there any non-gaussian distribution has skewness 0 and kurtosis 3?
The discrete distribution with probabilities
\begin{align}
p(-2)&=1/12\\
p(-1)&=\ 1/6\\
p(0)&=\ 1/2\\
p(1)&= \ 1/6\\
p(2)&=1/12
\end{align}
has the same mean, variance, skewness and kurtosis as the Ga |
51,570 | Is there any non-gaussian distribution has skewness 0 and kurtosis 3? | Note that in terms of cumulants $\kappa_n$, $n\ge 1$ one has
$$
{\rm Mean}=\kappa_1
$$
$$
{\rm Variance}=\kappa_2
$$
$$
{\rm Skewness}=\frac{\kappa_3}{\kappa_{2}^{\frac{3}{2}}}
$$
$$
{\rm Kurtosis}=3+\frac{\kappa_4}{\kappa_2^2}
$$
My understanding of the OP's question is whether there are RVs other than the $N(0,1)$ wh... | Is there any non-gaussian distribution has skewness 0 and kurtosis 3? | Note that in terms of cumulants $\kappa_n$, $n\ge 1$ one has
$$
{\rm Mean}=\kappa_1
$$
$$
{\rm Variance}=\kappa_2
$$
$$
{\rm Skewness}=\frac{\kappa_3}{\kappa_{2}^{\frac{3}{2}}}
$$
$$
{\rm Kurtosis}=3+ | Is there any non-gaussian distribution has skewness 0 and kurtosis 3?
Note that in terms of cumulants $\kappa_n$, $n\ge 1$ one has
$$
{\rm Mean}=\kappa_1
$$
$$
{\rm Variance}=\kappa_2
$$
$$
{\rm Skewness}=\frac{\kappa_3}{\kappa_{2}^{\frac{3}{2}}}
$$
$$
{\rm Kurtosis}=3+\frac{\kappa_4}{\kappa_2^2}
$$
My understanding of... | Is there any non-gaussian distribution has skewness 0 and kurtosis 3?
Note that in terms of cumulants $\kappa_n$, $n\ge 1$ one has
$$
{\rm Mean}=\kappa_1
$$
$$
{\rm Variance}=\kappa_2
$$
$$
{\rm Skewness}=\frac{\kappa_3}{\kappa_{2}^{\frac{3}{2}}}
$$
$$
{\rm Kurtosis}=3+ |
51,571 | Is there any non-gaussian distribution has skewness 0 and kurtosis 3? | Yes, there is. The Laplace$(0,\frac{1}{2})$ distribution has the
required properties. Its probability density function is given by
$f(x)=\exp(-2\lvert x\rvert)$ over the real line. | Is there any non-gaussian distribution has skewness 0 and kurtosis 3? | Yes, there is. The Laplace$(0,\frac{1}{2})$ distribution has the
required properties. Its probability density function is given by
$f(x)=\exp(-2\lvert x\rvert)$ over the real line. | Is there any non-gaussian distribution has skewness 0 and kurtosis 3?
Yes, there is. The Laplace$(0,\frac{1}{2})$ distribution has the
required properties. Its probability density function is given by
$f(x)=\exp(-2\lvert x\rvert)$ over the real line. | Is there any non-gaussian distribution has skewness 0 and kurtosis 3?
Yes, there is. The Laplace$(0,\frac{1}{2})$ distribution has the
required properties. Its probability density function is given by
$f(x)=\exp(-2\lvert x\rvert)$ over the real line. |
51,572 | Is there any non-gaussian distribution has skewness 0 and kurtosis 3? | Skewness and Kurtosis, opposite to the popular belief, are non-uniquely defined concepts. There are general definitions of Skewness and Kurtosis:
Convex transformations of random variables
and the definitions based on the third and fourth moments are just one of them. In fact, these definitions are not even defined fo... | Is there any non-gaussian distribution has skewness 0 and kurtosis 3? | Skewness and Kurtosis, opposite to the popular belief, are non-uniquely defined concepts. There are general definitions of Skewness and Kurtosis:
Convex transformations of random variables
and the de | Is there any non-gaussian distribution has skewness 0 and kurtosis 3?
Skewness and Kurtosis, opposite to the popular belief, are non-uniquely defined concepts. There are general definitions of Skewness and Kurtosis:
Convex transformations of random variables
and the definitions based on the third and fourth moments ar... | Is there any non-gaussian distribution has skewness 0 and kurtosis 3?
Skewness and Kurtosis, opposite to the popular belief, are non-uniquely defined concepts. There are general definitions of Skewness and Kurtosis:
Convex transformations of random variables
and the de |
51,573 | Birthday Problem: How am I wrong? [duplicate] | I think the logic is wrong since the probability of two persons having different birthdays is dependent on the fact that they need to have different birthdays than all the others. A simple example birthday paradox for A,B and C not having birthday on the same weekday. Each of these pairs are 1/7 in a vacuum. But given ... | Birthday Problem: How am I wrong? [duplicate] | I think the logic is wrong since the probability of two persons having different birthdays is dependent on the fact that they need to have different birthdays than all the others. A simple example bir | Birthday Problem: How am I wrong? [duplicate]
I think the logic is wrong since the probability of two persons having different birthdays is dependent on the fact that they need to have different birthdays than all the others. A simple example birthday paradox for A,B and C not having birthday on the same weekday. Each ... | Birthday Problem: How am I wrong? [duplicate]
I think the logic is wrong since the probability of two persons having different birthdays is dependent on the fact that they need to have different birthdays than all the others. A simple example bir |
51,574 | Birthday Problem: How am I wrong? [duplicate] | Notice that your answer is never equal to $1$ regardless of how high $n$ is. However, obviously if $n=366$ then there must be two people with the same birthday.
So basically, the correct answer captures the fact that for everyone to have a different birthday, you begin running out of dates the more people are in the ro... | Birthday Problem: How am I wrong? [duplicate] | Notice that your answer is never equal to $1$ regardless of how high $n$ is. However, obviously if $n=366$ then there must be two people with the same birthday.
So basically, the correct answer captur | Birthday Problem: How am I wrong? [duplicate]
Notice that your answer is never equal to $1$ regardless of how high $n$ is. However, obviously if $n=366$ then there must be two people with the same birthday.
So basically, the correct answer captures the fact that for everyone to have a different birthday, you begin runn... | Birthday Problem: How am I wrong? [duplicate]
Notice that your answer is never equal to $1$ regardless of how high $n$ is. However, obviously if $n=366$ then there must be two people with the same birthday.
So basically, the correct answer captur |
51,575 | Birthday Problem: How am I wrong? [duplicate] | If you have three days $x$, $y$, $z$, the events $x\ne y$, $x\ne z$, and $y\ne z$ are not independent, but you treat them as such. | Birthday Problem: How am I wrong? [duplicate] | If you have three days $x$, $y$, $z$, the events $x\ne y$, $x\ne z$, and $y\ne z$ are not independent, but you treat them as such. | Birthday Problem: How am I wrong? [duplicate]
If you have three days $x$, $y$, $z$, the events $x\ne y$, $x\ne z$, and $y\ne z$ are not independent, but you treat them as such. | Birthday Problem: How am I wrong? [duplicate]
If you have three days $x$, $y$, $z$, the events $x\ne y$, $x\ne z$, and $y\ne z$ are not independent, but you treat them as such. |
51,576 | Is time of the day (predictor in regression) a categorical or a continuous variable? | It is neither. Actually, it is what you make it to be in your model formula, there are more than two possibilities, and there is not necessarily one correct answer among them!
If you make it categorical, then your model will have a separate, independent coefficient (or more precisely, degree of freedom) for each hour o... | Is time of the day (predictor in regression) a categorical or a continuous variable? | It is neither. Actually, it is what you make it to be in your model formula, there are more than two possibilities, and there is not necessarily one correct answer among them!
If you make it categoric | Is time of the day (predictor in regression) a categorical or a continuous variable?
It is neither. Actually, it is what you make it to be in your model formula, there are more than two possibilities, and there is not necessarily one correct answer among them!
If you make it categorical, then your model will have a sep... | Is time of the day (predictor in regression) a categorical or a continuous variable?
It is neither. Actually, it is what you make it to be in your model formula, there are more than two possibilities, and there is not necessarily one correct answer among them!
If you make it categoric |
51,577 | Is time of the day (predictor in regression) a categorical or a continuous variable? | Well if you include it in levels, $0, ..., 23$ then what would be the interpretation of the $\hat{\beta}_{\text{time of day}}$?
You are including ordinal information, the coding is esstially arbitrary. You could change the value of 23 (11 PM) to 512, and it would still hold the same meaning. This is unlike (say) heigh... | Is time of the day (predictor in regression) a categorical or a continuous variable? | Well if you include it in levels, $0, ..., 23$ then what would be the interpretation of the $\hat{\beta}_{\text{time of day}}$?
You are including ordinal information, the coding is esstially arbitrar | Is time of the day (predictor in regression) a categorical or a continuous variable?
Well if you include it in levels, $0, ..., 23$ then what would be the interpretation of the $\hat{\beta}_{\text{time of day}}$?
You are including ordinal information, the coding is esstially arbitrary. You could change the value of 23... | Is time of the day (predictor in regression) a categorical or a continuous variable?
Well if you include it in levels, $0, ..., 23$ then what would be the interpretation of the $\hat{\beta}_{\text{time of day}}$?
You are including ordinal information, the coding is esstially arbitrar |
51,578 | Is time of the day (predictor in regression) a categorical or a continuous variable? | It depends on how you interpret the variable but I would be inclined to say continuous, since it is ordered and there is a natural, consistent separation between the values that can be assumed (1 hr between consecutive values). A continuous example would be if your response is the location of an object in freefall and ... | Is time of the day (predictor in regression) a categorical or a continuous variable? | It depends on how you interpret the variable but I would be inclined to say continuous, since it is ordered and there is a natural, consistent separation between the values that can be assumed (1 hr b | Is time of the day (predictor in regression) a categorical or a continuous variable?
It depends on how you interpret the variable but I would be inclined to say continuous, since it is ordered and there is a natural, consistent separation between the values that can be assumed (1 hr between consecutive values). A conti... | Is time of the day (predictor in regression) a categorical or a continuous variable?
It depends on how you interpret the variable but I would be inclined to say continuous, since it is ordered and there is a natural, consistent separation between the values that can be assumed (1 hr b |
51,579 | What is this equation (pictured) called? | On the linked page it introduces the equation as, "[t]he probability Q that a $\chi^2$ value calculated for an experiment with $d$ degrees of freedom... is due to chance". This suggests it is a version of the chi-squared distribution's CDF. Moreover, it looks a lot like the chi-squared distribution's pdf listed on th... | What is this equation (pictured) called? | On the linked page it introduces the equation as, "[t]he probability Q that a $\chi^2$ value calculated for an experiment with $d$ degrees of freedom... is due to chance". This suggests it is a versi | What is this equation (pictured) called?
On the linked page it introduces the equation as, "[t]he probability Q that a $\chi^2$ value calculated for an experiment with $d$ degrees of freedom... is due to chance". This suggests it is a version of the chi-squared distribution's CDF. Moreover, it looks a lot like the ch... | What is this equation (pictured) called?
On the linked page it introduces the equation as, "[t]he probability Q that a $\chi^2$ value calculated for an experiment with $d$ degrees of freedom... is due to chance". This suggests it is a versi |
51,580 | What is this equation (pictured) called? | The brackets are for grouping; they're just parentheses here. This is $1 - \operatorname{CDF}_{\chi^2}(x^2; d)$.
Let $\operatorname{PDF}_{\chi^2}(\cdot;d) = f(\cdot;d)$. Then
$$
f(t;d) \equiv \left( 2^\frac{d}{2} \operatorname{\Gamma}\left(\frac{d}{2} \right) \right)^{-1} t^{\frac{d}{2}-1} e^{-\frac{t}{2}}
$$
so that
... | What is this equation (pictured) called? | The brackets are for grouping; they're just parentheses here. This is $1 - \operatorname{CDF}_{\chi^2}(x^2; d)$.
Let $\operatorname{PDF}_{\chi^2}(\cdot;d) = f(\cdot;d)$. Then
$$
f(t;d) \equiv \left( 2 | What is this equation (pictured) called?
The brackets are for grouping; they're just parentheses here. This is $1 - \operatorname{CDF}_{\chi^2}(x^2; d)$.
Let $\operatorname{PDF}_{\chi^2}(\cdot;d) = f(\cdot;d)$. Then
$$
f(t;d) \equiv \left( 2^\frac{d}{2} \operatorname{\Gamma}\left(\frac{d}{2} \right) \right)^{-1} t^{\f... | What is this equation (pictured) called?
The brackets are for grouping; they're just parentheses here. This is $1 - \operatorname{CDF}_{\chi^2}(x^2; d)$.
Let $\operatorname{PDF}_{\chi^2}(\cdot;d) = f(\cdot;d)$. Then
$$
f(t;d) \equiv \left( 2 |
51,581 | How does cross validation works for feature selection (using stepwise regression)? | Running a single cross-validation loop yields an estimate of the out of sample predictive error associated with your modeling procedure, nothing more. You have 10 different models because stepwise selection is unstable, as @Dave explains. There is no reason to believe that any of your 10 models is 'right', but the me... | How does cross validation works for feature selection (using stepwise regression)? | Running a single cross-validation loop yields an estimate of the out of sample predictive error associated with your modeling procedure, nothing more. You have 10 different models because stepwise se | How does cross validation works for feature selection (using stepwise regression)?
Running a single cross-validation loop yields an estimate of the out of sample predictive error associated with your modeling procedure, nothing more. You have 10 different models because stepwise selection is unstable, as @Dave explain... | How does cross validation works for feature selection (using stepwise regression)?
Running a single cross-validation loop yields an estimate of the out of sample predictive error associated with your modeling procedure, nothing more. You have 10 different models because stepwise se |
51,582 | How does cross validation works for feature selection (using stepwise regression)? | Welcome to the instability of feature selection. This is totally predictable behavior and one of the reasons why stepwise regression is less of a panacea than it first seems to be. Sure, you select some variables that work well on the training data, and by limiting the variable count to just those that influence the ou... | How does cross validation works for feature selection (using stepwise regression)? | Welcome to the instability of feature selection. This is totally predictable behavior and one of the reasons why stepwise regression is less of a panacea than it first seems to be. Sure, you select so | How does cross validation works for feature selection (using stepwise regression)?
Welcome to the instability of feature selection. This is totally predictable behavior and one of the reasons why stepwise regression is less of a panacea than it first seems to be. Sure, you select some variables that work well on the tr... | How does cross validation works for feature selection (using stepwise regression)?
Welcome to the instability of feature selection. This is totally predictable behavior and one of the reasons why stepwise regression is less of a panacea than it first seems to be. Sure, you select so |
51,583 | How does cross validation works for feature selection (using stepwise regression)? | Training 10 models and picking the best one based on the test set performance metrics is "cheating" - your performance metrics are no longer an unbiased measure of your overall model training procedure, since your model training procedure now uses the test data to select the model! A test set should only be used to eva... | How does cross validation works for feature selection (using stepwise regression)? | Training 10 models and picking the best one based on the test set performance metrics is "cheating" - your performance metrics are no longer an unbiased measure of your overall model training procedur | How does cross validation works for feature selection (using stepwise regression)?
Training 10 models and picking the best one based on the test set performance metrics is "cheating" - your performance metrics are no longer an unbiased measure of your overall model training procedure, since your model training procedur... | How does cross validation works for feature selection (using stepwise regression)?
Training 10 models and picking the best one based on the test set performance metrics is "cheating" - your performance metrics are no longer an unbiased measure of your overall model training procedur |
51,584 | Antithetic method for monte carlo when bounds of the integral are infinite | Your code does not correspond to your description of the problem, so it is not surprising that you are not getting the results you expected. The integral you want to approximate is
$$
\int_0^\infty e^{-x} \, dx
$$
but in your code, you sample the a values from $\mathcal{U}(0, 1)$ distribution, I'd argue that $1$ is muc... | Antithetic method for monte carlo when bounds of the integral are infinite | Your code does not correspond to your description of the problem, so it is not surprising that you are not getting the results you expected. The integral you want to approximate is
$$
\int_0^\infty e^ | Antithetic method for monte carlo when bounds of the integral are infinite
Your code does not correspond to your description of the problem, so it is not surprising that you are not getting the results you expected. The integral you want to approximate is
$$
\int_0^\infty e^{-x} \, dx
$$
but in your code, you sample th... | Antithetic method for monte carlo when bounds of the integral are infinite
Your code does not correspond to your description of the problem, so it is not surprising that you are not getting the results you expected. The integral you want to approximate is
$$
\int_0^\infty e^ |
51,585 | Antithetic method for monte carlo when bounds of the integral are infinite | The point to the method of antithetic variates is to improve on such direct sampling methods. What can make it work well is to re-express the integral as an expectation of something with respect to a distribution that (a) you can efficiently sample from and (b) samples the largest absolute values of the integrand with... | Antithetic method for monte carlo when bounds of the integral are infinite | The point to the method of antithetic variates is to improve on such direct sampling methods. What can make it work well is to re-express the integral as an expectation of something with respect to a | Antithetic method for monte carlo when bounds of the integral are infinite
The point to the method of antithetic variates is to improve on such direct sampling methods. What can make it work well is to re-express the integral as an expectation of something with respect to a distribution that (a) you can efficiently sa... | Antithetic method for monte carlo when bounds of the integral are infinite
The point to the method of antithetic variates is to improve on such direct sampling methods. What can make it work well is to re-express the integral as an expectation of something with respect to a |
51,586 | Slope of independent variable is larger when I divide sample into subsets | This is a very common scenario when you split your data into groups that differ systematically. Here's an example:
set.seed(4218)
N = 100
group <- rep(1:2, each = N%/%2)
x <- rnorm(N)
y <- sqrt(.2) * x + sqrt(.8) * rnorm(N)
x[group==2] <- x[group==2]+5
splitByGroup <- split(cbind.data.frame(x=x,y=y), group)
modelAll <... | Slope of independent variable is larger when I divide sample into subsets | This is a very common scenario when you split your data into groups that differ systematically. Here's an example:
set.seed(4218)
N = 100
group <- rep(1:2, each = N%/%2)
x <- rnorm(N)
y <- sqrt(.2) * | Slope of independent variable is larger when I divide sample into subsets
This is a very common scenario when you split your data into groups that differ systematically. Here's an example:
set.seed(4218)
N = 100
group <- rep(1:2, each = N%/%2)
x <- rnorm(N)
y <- sqrt(.2) * x + sqrt(.8) * rnorm(N)
x[group==2] <- x[group... | Slope of independent variable is larger when I divide sample into subsets
This is a very common scenario when you split your data into groups that differ systematically. Here's an example:
set.seed(4218)
N = 100
group <- rep(1:2, each = N%/%2)
x <- rnorm(N)
y <- sqrt(.2) * |
51,587 | Slope of independent variable is larger when I divide sample into subsets | This is possible due to the possible nature of different distributions for different subsets of the data. This will be hard to describe without a graph, but imagine a small cloud of points in the upper-left corner of a square. Let's assume the points follow closely to a cigar-shaped cloud of points that would suggest a... | Slope of independent variable is larger when I divide sample into subsets | This is possible due to the possible nature of different distributions for different subsets of the data. This will be hard to describe without a graph, but imagine a small cloud of points in the uppe | Slope of independent variable is larger when I divide sample into subsets
This is possible due to the possible nature of different distributions for different subsets of the data. This will be hard to describe without a graph, but imagine a small cloud of points in the upper-left corner of a square. Let's assume the po... | Slope of independent variable is larger when I divide sample into subsets
This is possible due to the possible nature of different distributions for different subsets of the data. This will be hard to describe without a graph, but imagine a small cloud of points in the uppe |
51,588 | For least squares estimation, what is the difference between using the estimator $\hat{\beta} = X^{T}Y$ vs $\hat{\beta} = (X^{T}X)^{-1}X^{T}Y$ | I don't believe that the first estimator is unbiased.
Under the linear model assumption
$$ Y = X \beta + \epsilon $$
the expectation of the first estimator is
$$ E[X^{T} Y] = E[X^{T}X \beta] + E[X^{T} \epsilon] = \underbrace{X^{T}X \beta + X^{T} E[\epsilon]}_{\text{linearity of expectation}} = X^{T}X \beta$$
From which... | For least squares estimation, what is the difference between using the estimator $\hat{\beta} = X^{T | I don't believe that the first estimator is unbiased.
Under the linear model assumption
$$ Y = X \beta + \epsilon $$
the expectation of the first estimator is
$$ E[X^{T} Y] = E[X^{T}X \beta] + E[X^{T} | For least squares estimation, what is the difference between using the estimator $\hat{\beta} = X^{T}Y$ vs $\hat{\beta} = (X^{T}X)^{-1}X^{T}Y$
I don't believe that the first estimator is unbiased.
Under the linear model assumption
$$ Y = X \beta + \epsilon $$
the expectation of the first estimator is
$$ E[X^{T} Y] = E[... | For least squares estimation, what is the difference between using the estimator $\hat{\beta} = X^{T
I don't believe that the first estimator is unbiased.
Under the linear model assumption
$$ Y = X \beta + \epsilon $$
the expectation of the first estimator is
$$ E[X^{T} Y] = E[X^{T}X \beta] + E[X^{T} |
51,589 | For least squares estimation, what is the difference between using the estimator $\hat{\beta} = X^{T}Y$ vs $\hat{\beta} = (X^{T}X)^{-1}X^{T}Y$ | Your first estimator doesn't work. It's not only biased, but the bias increases with the sample size.
Imagine a simple intercept model:
$$y_i=c+\varepsilon_i$$
here your design matrix is a simple vector of ones, $x_i=1$.
Your estimator is $$\hat c = X'Y\equiv\sum_{i=1}^n y_i$$
If you add more data your estimator keeps... | For least squares estimation, what is the difference between using the estimator $\hat{\beta} = X^{T | Your first estimator doesn't work. It's not only biased, but the bias increases with the sample size.
Imagine a simple intercept model:
$$y_i=c+\varepsilon_i$$
here your design matrix is a simple vect | For least squares estimation, what is the difference between using the estimator $\hat{\beta} = X^{T}Y$ vs $\hat{\beta} = (X^{T}X)^{-1}X^{T}Y$
Your first estimator doesn't work. It's not only biased, but the bias increases with the sample size.
Imagine a simple intercept model:
$$y_i=c+\varepsilon_i$$
here your design ... | For least squares estimation, what is the difference between using the estimator $\hat{\beta} = X^{T
Your first estimator doesn't work. It's not only biased, but the bias increases with the sample size.
Imagine a simple intercept model:
$$y_i=c+\varepsilon_i$$
here your design matrix is a simple vect |
51,590 | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers? | Search for the "transformation method" or "inverse transform method", which is a way to generate random numbers with an arbitrary distribution. You'll find many lecture notes describing the idea. This is the wikipedia page: Inverse transform sampling. It has links to more detailed resources at the bottom.
The basic re... | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers? | Search for the "transformation method" or "inverse transform method", which is a way to generate random numbers with an arbitrary distribution. You'll find many lecture notes describing the idea. This | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers?
Search for the "transformation method" or "inverse transform method", which is a way to generate random numbers with an arbitrary distribution. You'll find many lecture notes describing the idea. This is the wikipedia page: Inverse t... | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers?
Search for the "transformation method" or "inverse transform method", which is a way to generate random numbers with an arbitrary distribution. You'll find many lecture notes describing the idea. This |
51,591 | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers? | If $X$ and $Y$ are independent uniformly distributed random variables on $[0,1]$, then $X + Y$ has a pyramid or "inverted V" shaped distribution on $[0,2]$.
All we need to do to turn this pyramid into a V is to swap the two halves of the distribution.
Thus, given independent $X, Y \sim \mathcal U(0,1)$, let $$Z = \begi... | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers? | If $X$ and $Y$ are independent uniformly distributed random variables on $[0,1]$, then $X + Y$ has a pyramid or "inverted V" shaped distribution on $[0,2]$.
All we need to do to turn this pyramid into | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers?
If $X$ and $Y$ are independent uniformly distributed random variables on $[0,1]$, then $X + Y$ has a pyramid or "inverted V" shaped distribution on $[0,2]$.
All we need to do to turn this pyramid into a V is to swap the two halves of... | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers?
If $X$ and $Y$ are independent uniformly distributed random variables on $[0,1]$, then $X + Y$ has a pyramid or "inverted V" shaped distribution on $[0,2]$.
All we need to do to turn this pyramid into |
51,592 | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers? | This should really be a comment below the above answer. But since I do not have enough reputation to make this comment I will post it here. In the question you originally asked how to do this in "excel". This should do it,
=SIGN(2*RAND()-1)*(ABS(1-2*RAND()))^0.5
An interesting note to the previous answer is that i... | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers? | This should really be a comment below the above answer. But since I do not have enough reputation to make this comment I will post it here. In the question you originally asked how to do this in "ex | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers?
This should really be a comment below the above answer. But since I do not have enough reputation to make this comment I will post it here. In the question you originally asked how to do this in "excel". This should do it,
=SIGN(... | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers?
This should really be a comment below the above answer. But since I do not have enough reputation to make this comment I will post it here. In the question you originally asked how to do this in "ex |
51,593 | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers? | You do want to use $U\sim \text{Uniform}(0,1)$. You are looking for something called Probability Integral Transform (PIT).
If $X \sim F$ then, $F(X)\sim \text{Uniform}(0,1)$. Therefore, first you will find the CDF for the distribution you are interested in, then you will transform. For example if you want $f(x)=|x|$, ... | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers? | You do want to use $U\sim \text{Uniform}(0,1)$. You are looking for something called Probability Integral Transform (PIT).
If $X \sim F$ then, $F(X)\sim \text{Uniform}(0,1)$. Therefore, first you wil | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers?
You do want to use $U\sim \text{Uniform}(0,1)$. You are looking for something called Probability Integral Transform (PIT).
If $X \sim F$ then, $F(X)\sim \text{Uniform}(0,1)$. Therefore, first you will find the CDF for the distributi... | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers?
You do want to use $U\sim \text{Uniform}(0,1)$. You are looking for something called Probability Integral Transform (PIT).
If $X \sim F$ then, $F(X)\sim \text{Uniform}(0,1)$. Therefore, first you wil |
51,594 | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers? | There are a variety of approaches that might be suitable.
Even with the comments you haven't really pinned it down enough (you give an example of what you want, but not what range of cases you want considered), but here are some examples of approaches:
1) rejection sampling. Generate a uniform on the desired range, an... | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers? | There are a variety of approaches that might be suitable.
Even with the comments you haven't really pinned it down enough (you give an example of what you want, but not what range of cases you want c | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers?
There are a variety of approaches that might be suitable.
Even with the comments you haven't really pinned it down enough (you give an example of what you want, but not what range of cases you want considered), but here are some exa... | How do I get "V-shaped" distributed random numbers from uniformly distributed numbers?
There are a variety of approaches that might be suitable.
Even with the comments you haven't really pinned it down enough (you give an example of what you want, but not what range of cases you want c |
51,595 | Interpreting weird box plot with reversed whiskers | It is impossible to know without knowing more about what your software thinks is the right way to draw a box and whisker plot. It is even more difficult without a numeric scale to anchor the results on. Regardless, there are a number of different guidelines in this regard (in general). However, we can always resort ... | Interpreting weird box plot with reversed whiskers | It is impossible to know without knowing more about what your software thinks is the right way to draw a box and whisker plot. It is even more difficult without a numeric scale to anchor the results | Interpreting weird box plot with reversed whiskers
It is impossible to know without knowing more about what your software thinks is the right way to draw a box and whisker plot. It is even more difficult without a numeric scale to anchor the results on. Regardless, there are a number of different guidelines in this r... | Interpreting weird box plot with reversed whiskers
It is impossible to know without knowing more about what your software thinks is the right way to draw a box and whisker plot. It is even more difficult without a numeric scale to anchor the results |
51,596 | Interpreting weird box plot with reversed whiskers | In R I've done the boxplot and plotted the individual points so you can see what it's doing:
> x<-c(16.5, 17.14, 13.5, 16.75)
> boxplot(x,boxwex=.2)
> points(x~rep(1,4),pch="x",col=2)
As you see, it's not like the one you have.
In particular, after I stretch your bitmap out to approximately match the range (assuming ... | Interpreting weird box plot with reversed whiskers | In R I've done the boxplot and plotted the individual points so you can see what it's doing:
> x<-c(16.5, 17.14, 13.5, 16.75)
> boxplot(x,boxwex=.2)
> points(x~rep(1,4),pch="x",col=2)
As you see, it | Interpreting weird box plot with reversed whiskers
In R I've done the boxplot and plotted the individual points so you can see what it's doing:
> x<-c(16.5, 17.14, 13.5, 16.75)
> boxplot(x,boxwex=.2)
> points(x~rep(1,4),pch="x",col=2)
As you see, it's not like the one you have.
In particular, after I stretch your bit... | Interpreting weird box plot with reversed whiskers
In R I've done the boxplot and plotted the individual points so you can see what it's doing:
> x<-c(16.5, 17.14, 13.5, 16.75)
> boxplot(x,boxwex=.2)
> points(x~rep(1,4),pch="x",col=2)
As you see, it |
51,597 | What is the "root MSE" in Stata? | Calculate the difference between the observed and predicted dependent variables
Square them
Add them up, this will give you the "Error sum of squares," SS in Stata output
Divide it by the error's degrees of freedom, this will give you the "Mean error sum of squares," MS in Stata output
Take a square root of it, and thi... | What is the "root MSE" in Stata? | Calculate the difference between the observed and predicted dependent variables
Square them
Add them up, this will give you the "Error sum of squares," SS in Stata output
Divide it by the error's degr | What is the "root MSE" in Stata?
Calculate the difference between the observed and predicted dependent variables
Square them
Add them up, this will give you the "Error sum of squares," SS in Stata output
Divide it by the error's degrees of freedom, this will give you the "Mean error sum of squares," MS in Stata output
... | What is the "root MSE" in Stata?
Calculate the difference between the observed and predicted dependent variables
Square them
Add them up, this will give you the "Error sum of squares," SS in Stata output
Divide it by the error's degr |
51,598 | What is the "root MSE" in Stata? | RMSE is the std dev of the model's error.
Wikipedia can tell you this and the formula: http://en.wikipedia.org/wiki/Root-mean-square_deviation
With it, you can compare model accuracy | What is the "root MSE" in Stata? | RMSE is the std dev of the model's error.
Wikipedia can tell you this and the formula: http://en.wikipedia.org/wiki/Root-mean-square_deviation
With it, you can compare model accuracy | What is the "root MSE" in Stata?
RMSE is the std dev of the model's error.
Wikipedia can tell you this and the formula: http://en.wikipedia.org/wiki/Root-mean-square_deviation
With it, you can compare model accuracy | What is the "root MSE" in Stata?
RMSE is the std dev of the model's error.
Wikipedia can tell you this and the formula: http://en.wikipedia.org/wiki/Root-mean-square_deviation
With it, you can compare model accuracy |
51,599 | Is it appropriate to examine an interaction effect that is almost statistically significant? | Worship not p = 0.05. Explore away.
Additionally, in some contexts, relying on p = 0.05 for an interaction threshold is actually a bit flawed, as interaction tests are typically fairly low powered, and you can and should be using a somewhat higher threshold to accept statistical evidence of interaction. Sander Greenlan... | Is it appropriate to examine an interaction effect that is almost statistically significant? | Worship not p = 0.05. Explore away.
Additionally, in some contexts, relying on p = 0.05 for an interaction threshold is actually a bit flawed, as interaction tests are typically fairly low powered, an | Is it appropriate to examine an interaction effect that is almost statistically significant?
Worship not p = 0.05. Explore away.
Additionally, in some contexts, relying on p = 0.05 for an interaction threshold is actually a bit flawed, as interaction tests are typically fairly low powered, and you can and should be usi... | Is it appropriate to examine an interaction effect that is almost statistically significant?
Worship not p = 0.05. Explore away.
Additionally, in some contexts, relying on p = 0.05 for an interaction threshold is actually a bit flawed, as interaction tests are typically fairly low powered, an |
51,600 | Is it appropriate to examine an interaction effect that is almost statistically significant? | "Can't"? Who says you can't? There's nothing magic about p = .05. You can certainly explore the interaction.
The question is how you deal with complaints from people who say you can't do this. In addition to works by Greenland or Hernan (see @EpiGrad's response) you can look for papers by Jacob Cohen or Paul Meehl or t... | Is it appropriate to examine an interaction effect that is almost statistically significant? | "Can't"? Who says you can't? There's nothing magic about p = .05. You can certainly explore the interaction.
The question is how you deal with complaints from people who say you can't do this. In addi | Is it appropriate to examine an interaction effect that is almost statistically significant?
"Can't"? Who says you can't? There's nothing magic about p = .05. You can certainly explore the interaction.
The question is how you deal with complaints from people who say you can't do this. In addition to works by Greenland ... | Is it appropriate to examine an interaction effect that is almost statistically significant?
"Can't"? Who says you can't? There's nothing magic about p = .05. You can certainly explore the interaction.
The question is how you deal with complaints from people who say you can't do this. In addi |
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