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 |
|---|---|---|---|---|---|---|
44,001 | What does it mean, when, three standard deviations away from the mean, I land outside of the minimum or maximum value? | “Three st.dev.s ($3\sqrt{\sigma^2}$) include 99.7% of the data” refers to Gaussian distributions. For distributions in general, Chebyshev's inequality puts a lower bound on the amount of probability mass withing $k$ of the mean. But is there an upper bound?
With a Bernoulli distribution with $p$ = .5, the $\sigma$ is .... | What does it mean, when, three standard deviations away from the mean, I land outside of the minimum | “Three st.dev.s ($3\sqrt{\sigma^2}$) include 99.7% of the data” refers to Gaussian distributions. For distributions in general, Chebyshev's inequality puts a lower bound on the amount of probability m | What does it mean, when, three standard deviations away from the mean, I land outside of the minimum or maximum value?
“Three st.dev.s ($3\sqrt{\sigma^2}$) include 99.7% of the data” refers to Gaussian distributions. For distributions in general, Chebyshev's inequality puts a lower bound on the amount of probability ma... | What does it mean, when, three standard deviations away from the mean, I land outside of the minimum
“Three st.dev.s ($3\sqrt{\sigma^2}$) include 99.7% of the data” refers to Gaussian distributions. For distributions in general, Chebyshev's inequality puts a lower bound on the amount of probability m |
44,002 | Can I use lasso when it is not a high dimensional setting? | There's nothing that suggests you need a number of predictors ($p$) as large as 200 or sample size ($n$) as large 500, let alone larger. (You might find it surprising to read some of the early papers on both methods.)
You can very successfully use regularization methods like ridge regression and lasso on problems with ... | Can I use lasso when it is not a high dimensional setting? | There's nothing that suggests you need a number of predictors ($p$) as large as 200 or sample size ($n$) as large 500, let alone larger. (You might find it surprising to read some of the early papers | Can I use lasso when it is not a high dimensional setting?
There's nothing that suggests you need a number of predictors ($p$) as large as 200 or sample size ($n$) as large 500, let alone larger. (You might find it surprising to read some of the early papers on both methods.)
You can very successfully use regularizatio... | Can I use lasso when it is not a high dimensional setting?
There's nothing that suggests you need a number of predictors ($p$) as large as 200 or sample size ($n$) as large 500, let alone larger. (You might find it surprising to read some of the early papers |
44,003 | Can I use lasso when it is not a high dimensional setting? | Whether a given setting is high-dimensional or not depends on both the number of samples you have and the number of dimensions. Increasing the number of dimensions requires exponentially more data to "fill up" the feature space - look up the curse of dimensionality.
200 predictors for 500 observations is a huge number ... | Can I use lasso when it is not a high dimensional setting? | Whether a given setting is high-dimensional or not depends on both the number of samples you have and the number of dimensions. Increasing the number of dimensions requires exponentially more data to | Can I use lasso when it is not a high dimensional setting?
Whether a given setting is high-dimensional or not depends on both the number of samples you have and the number of dimensions. Increasing the number of dimensions requires exponentially more data to "fill up" the feature space - look up the curse of dimensiona... | Can I use lasso when it is not a high dimensional setting?
Whether a given setting is high-dimensional or not depends on both the number of samples you have and the number of dimensions. Increasing the number of dimensions requires exponentially more data to |
44,004 | Can I use lasso when it is not a high dimensional setting? | I suppose you are talking about the setting when p n or p > n (as high dimensional), lasso has an additional advantage of solving the singularity problem that occurs in the above setting,which was prior motivation for developing regularisation (Thats why it is much used in higher dimensions). More on this here. As for... | Can I use lasso when it is not a high dimensional setting? | I suppose you are talking about the setting when p n or p > n (as high dimensional), lasso has an additional advantage of solving the singularity problem that occurs in the above setting,which was pr | Can I use lasso when it is not a high dimensional setting?
I suppose you are talking about the setting when p n or p > n (as high dimensional), lasso has an additional advantage of solving the singularity problem that occurs in the above setting,which was prior motivation for developing regularisation (Thats why it is... | Can I use lasso when it is not a high dimensional setting?
I suppose you are talking about the setting when p n or p > n (as high dimensional), lasso has an additional advantage of solving the singularity problem that occurs in the above setting,which was pr |
44,005 | Magnitude of standardized coefficients (beta) in multiple linear regression | It's never easy telling your professor that they are wrong.
Standardized coefficients can be greater than 1.00, as that article explains and as is easy to demonstrate. Whether they should be excluded depends on why they happened - but probably not.
They are a sign that you have some pretty serious collinearity. One c... | Magnitude of standardized coefficients (beta) in multiple linear regression | It's never easy telling your professor that they are wrong.
Standardized coefficients can be greater than 1.00, as that article explains and as is easy to demonstrate. Whether they should be excluded | Magnitude of standardized coefficients (beta) in multiple linear regression
It's never easy telling your professor that they are wrong.
Standardized coefficients can be greater than 1.00, as that article explains and as is easy to demonstrate. Whether they should be excluded depends on why they happened - but probably... | Magnitude of standardized coefficients (beta) in multiple linear regression
It's never easy telling your professor that they are wrong.
Standardized coefficients can be greater than 1.00, as that article explains and as is easy to demonstrate. Whether they should be excluded |
44,006 | Magnitude of standardized coefficients (beta) in multiple linear regression | This is probably a matter of definitions. Does a standardized coefficient refer to standardizing only the predictor variables? or standardizing the response variable as well? I have seen both used to compute "standardized coefficients". Even then, there is more than one way to standardize.
If you divide both the pr... | Magnitude of standardized coefficients (beta) in multiple linear regression | This is probably a matter of definitions. Does a standardized coefficient refer to standardizing only the predictor variables? or standardizing the response variable as well? I have seen both used | Magnitude of standardized coefficients (beta) in multiple linear regression
This is probably a matter of definitions. Does a standardized coefficient refer to standardizing only the predictor variables? or standardizing the response variable as well? I have seen both used to compute "standardized coefficients". Eve... | Magnitude of standardized coefficients (beta) in multiple linear regression
This is probably a matter of definitions. Does a standardized coefficient refer to standardizing only the predictor variables? or standardizing the response variable as well? I have seen both used |
44,007 | Magnitude of standardized coefficients (beta) in multiple linear regression | A standardized beta weight greater than one is a sign of suppression, especially cooperative suppression. Such suppression increases the predictive value of the predictors and thus is of potentially great value. See http://core.ecu.edu/psyc/wuenschk/MV/multReg/Suppress.docx | Magnitude of standardized coefficients (beta) in multiple linear regression | A standardized beta weight greater than one is a sign of suppression, especially cooperative suppression. Such suppression increases the predictive value of the predictors and thus is of potentially | Magnitude of standardized coefficients (beta) in multiple linear regression
A standardized beta weight greater than one is a sign of suppression, especially cooperative suppression. Such suppression increases the predictive value of the predictors and thus is of potentially great value. See http://core.ecu.edu/psyc/w... | Magnitude of standardized coefficients (beta) in multiple linear regression
A standardized beta weight greater than one is a sign of suppression, especially cooperative suppression. Such suppression increases the predictive value of the predictors and thus is of potentially |
44,008 | Given the advancements in statistical testing, can estimating correlations be an end in itself? | It is true, as @lejohn said, that if all you have is a hammer, everything looks like a nail.
It is also true, though, that if all you have is a nail, then you might only need a hammer!
The thing to do is to define your substantive question, whether it be from market research, psychology, physics or whatever. Then inves... | Given the advancements in statistical testing, can estimating correlations be an end in itself? | It is true, as @lejohn said, that if all you have is a hammer, everything looks like a nail.
It is also true, though, that if all you have is a nail, then you might only need a hammer!
The thing to do | Given the advancements in statistical testing, can estimating correlations be an end in itself?
It is true, as @lejohn said, that if all you have is a hammer, everything looks like a nail.
It is also true, though, that if all you have is a nail, then you might only need a hammer!
The thing to do is to define your subst... | Given the advancements in statistical testing, can estimating correlations be an end in itself?
It is true, as @lejohn said, that if all you have is a hammer, everything looks like a nail.
It is also true, though, that if all you have is a nail, then you might only need a hammer!
The thing to do |
44,009 | Given the advancements in statistical testing, can estimating correlations be an end in itself? | For me the answer to your question is no.
I do not think that a given method or technique can be an end in itself. If you have some data at hand or even before you start collecting data, you should ask yourself what are the problems you want to tackle, or what are the questions you would like to answer. When you have t... | Given the advancements in statistical testing, can estimating correlations be an end in itself? | For me the answer to your question is no.
I do not think that a given method or technique can be an end in itself. If you have some data at hand or even before you start collecting data, you should as | Given the advancements in statistical testing, can estimating correlations be an end in itself?
For me the answer to your question is no.
I do not think that a given method or technique can be an end in itself. If you have some data at hand or even before you start collecting data, you should ask yourself what are the ... | Given the advancements in statistical testing, can estimating correlations be an end in itself?
For me the answer to your question is no.
I do not think that a given method or technique can be an end in itself. If you have some data at hand or even before you start collecting data, you should as |
44,010 | Given the advancements in statistical testing, can estimating correlations be an end in itself? | Plotting you data should never be overlooked. In this example, the correlation between X and Y is 0, but surely the two variable are related.
| x x
| x x
| x x
| x x
Y| x x
| x x
| ... | Given the advancements in statistical testing, can estimating correlations be an end in itself? | Plotting you data should never be overlooked. In this example, the correlation between X and Y is 0, but surely the two variable are related.
| x x
| x | Given the advancements in statistical testing, can estimating correlations be an end in itself?
Plotting you data should never be overlooked. In this example, the correlation between X and Y is 0, but surely the two variable are related.
| x x
| x x
| x ... | Given the advancements in statistical testing, can estimating correlations be an end in itself?
Plotting you data should never be overlooked. In this example, the correlation between X and Y is 0, but surely the two variable are related.
| x x
| x |
44,011 | Given the advancements in statistical testing, can estimating correlations be an end in itself? | I agree with the others who have posted in this thread but have one point to add: Simple methods -- like correlation -- are more justifiable when you can be more sure that there aren't more complicated things going on.
In experimental work where you have used randomization to take into account potentially complicating ... | Given the advancements in statistical testing, can estimating correlations be an end in itself? | I agree with the others who have posted in this thread but have one point to add: Simple methods -- like correlation -- are more justifiable when you can be more sure that there aren't more complicate | Given the advancements in statistical testing, can estimating correlations be an end in itself?
I agree with the others who have posted in this thread but have one point to add: Simple methods -- like correlation -- are more justifiable when you can be more sure that there aren't more complicated things going on.
In ex... | Given the advancements in statistical testing, can estimating correlations be an end in itself?
I agree with the others who have posted in this thread but have one point to add: Simple methods -- like correlation -- are more justifiable when you can be more sure that there aren't more complicate |
44,012 | Why is the Breusch-Pagan test significant on simulated data designed not to be heteroscedastic? | No, the data are not heteroscedastic (by way of how you simulated them). Did you notice the 0 degrees of freedom of the test? That is a hint that something is going wrong here. The B-P test takes the squared residuals from the model and tests whether the predictors in the model (or any other predictors you specify) can... | Why is the Breusch-Pagan test significant on simulated data designed not to be heteroscedastic? | No, the data are not heteroscedastic (by way of how you simulated them). Did you notice the 0 degrees of freedom of the test? That is a hint that something is going wrong here. The B-P test takes the | Why is the Breusch-Pagan test significant on simulated data designed not to be heteroscedastic?
No, the data are not heteroscedastic (by way of how you simulated them). Did you notice the 0 degrees of freedom of the test? That is a hint that something is going wrong here. The B-P test takes the squared residuals from ... | Why is the Breusch-Pagan test significant on simulated data designed not to be heteroscedastic?
No, the data are not heteroscedastic (by way of how you simulated them). Did you notice the 0 degrees of freedom of the test? That is a hint that something is going wrong here. The B-P test takes the |
44,013 | Why is the Breusch-Pagan test significant on simulated data designed not to be heteroscedastic? | The results are not meaningful without some predictor (note df=0). Heteroscedastic means that the variance is not constant, but not constant with respect to what? Perhaps you have in mind the index (order of measurement)? Then you should do
y <- rnorm(1000)
x <- 1:1000
mod <- lm(y~x)
bptest(mod) # I get p=0.59
If y... | Why is the Breusch-Pagan test significant on simulated data designed not to be heteroscedastic? | The results are not meaningful without some predictor (note df=0). Heteroscedastic means that the variance is not constant, but not constant with respect to what? Perhaps you have in mind the index | Why is the Breusch-Pagan test significant on simulated data designed not to be heteroscedastic?
The results are not meaningful without some predictor (note df=0). Heteroscedastic means that the variance is not constant, but not constant with respect to what? Perhaps you have in mind the index (order of measurement)?... | Why is the Breusch-Pagan test significant on simulated data designed not to be heteroscedastic?
The results are not meaningful without some predictor (note df=0). Heteroscedastic means that the variance is not constant, but not constant with respect to what? Perhaps you have in mind the index |
44,014 | Why is the Breusch-Pagan test significant on simulated data designed not to be heteroscedastic? | :Dail To test for non-constant variance one must understand the hypothesis behind the popular statistical tests. you need to follow the recipe i,e, the tests that I outlined in How to check if the volatility is stationary?
to fully verify that a series can't be proven to have non-constant variance. All six of the tests... | Why is the Breusch-Pagan test significant on simulated data designed not to be heteroscedastic? | :Dail To test for non-constant variance one must understand the hypothesis behind the popular statistical tests. you need to follow the recipe i,e, the tests that I outlined in How to check if the vol | Why is the Breusch-Pagan test significant on simulated data designed not to be heteroscedastic?
:Dail To test for non-constant variance one must understand the hypothesis behind the popular statistical tests. you need to follow the recipe i,e, the tests that I outlined in How to check if the volatility is stationary?
... | Why is the Breusch-Pagan test significant on simulated data designed not to be heteroscedastic?
:Dail To test for non-constant variance one must understand the hypothesis behind the popular statistical tests. you need to follow the recipe i,e, the tests that I outlined in How to check if the vol |
44,015 | Free Dataset Resources? [duplicate] | Amazon has free Public Data sets for use with EC2.
http://aws.amazon.com/publicdatasets/
Here's a list: http://developer.amazonwebservices.com/connect/kbcategory.jspa?categoryID=243 | Free Dataset Resources? [duplicate] | Amazon has free Public Data sets for use with EC2.
http://aws.amazon.com/publicdatasets/
Here's a list: http://developer.amazonwebservices.com/connect/kbcategory.jspa?categoryID=243 | Free Dataset Resources? [duplicate]
Amazon has free Public Data sets for use with EC2.
http://aws.amazon.com/publicdatasets/
Here's a list: http://developer.amazonwebservices.com/connect/kbcategory.jspa?categoryID=243 | Free Dataset Resources? [duplicate]
Amazon has free Public Data sets for use with EC2.
http://aws.amazon.com/publicdatasets/
Here's a list: http://developer.amazonwebservices.com/connect/kbcategory.jspa?categoryID=243 |
44,016 | Free Dataset Resources? [duplicate] | I really like the FRED, from the St. Louis Fed (economics data). You can chart the series or more than one series, you can do some transformations to your data and chart it, and the NBER recessions are shaded. | Free Dataset Resources? [duplicate] | I really like the FRED, from the St. Louis Fed (economics data). You can chart the series or more than one series, you can do some transformations to your data and chart it, and the NBER recessions ar | Free Dataset Resources? [duplicate]
I really like the FRED, from the St. Louis Fed (economics data). You can chart the series or more than one series, you can do some transformations to your data and chart it, and the NBER recessions are shaded. | Free Dataset Resources? [duplicate]
I really like the FRED, from the St. Louis Fed (economics data). You can chart the series or more than one series, you can do some transformations to your data and chart it, and the NBER recessions ar |
44,017 | Free Dataset Resources? [duplicate] | For time series data, try the Time Series Data Library. | Free Dataset Resources? [duplicate] | For time series data, try the Time Series Data Library. | Free Dataset Resources? [duplicate]
For time series data, try the Time Series Data Library. | Free Dataset Resources? [duplicate]
For time series data, try the Time Series Data Library. |
44,018 | Free Dataset Resources? [duplicate] | http://infochimps.org/ - is a good resource for free data sets. | Free Dataset Resources? [duplicate] | http://infochimps.org/ - is a good resource for free data sets. | Free Dataset Resources? [duplicate]
http://infochimps.org/ - is a good resource for free data sets. | Free Dataset Resources? [duplicate]
http://infochimps.org/ - is a good resource for free data sets. |
44,019 | Free Dataset Resources? [duplicate] | For governmental data:
US: http://www.data.gov/
World: http://www.guardian.co.uk/world-government-data | Free Dataset Resources? [duplicate] | For governmental data:
US: http://www.data.gov/
World: http://www.guardian.co.uk/world-government-data | Free Dataset Resources? [duplicate]
For governmental data:
US: http://www.data.gov/
World: http://www.guardian.co.uk/world-government-data | Free Dataset Resources? [duplicate]
For governmental data:
US: http://www.data.gov/
World: http://www.guardian.co.uk/world-government-data |
44,020 | Are there any statistics to see if a categorical variable produces good segments within a scatter plot? [closed] | In cluster analysis, the Silhouette coefficient (SC; or Average Silhouette Width) is a distance-based statistic that measures the quality of a clustering, i.e., to what extent the objects are closer to other objects in the same class than to the closest class to which they don't belong.
This can also be computed for si... | Are there any statistics to see if a categorical variable produces good segments within a scatter pl | In cluster analysis, the Silhouette coefficient (SC; or Average Silhouette Width) is a distance-based statistic that measures the quality of a clustering, i.e., to what extent the objects are closer t | Are there any statistics to see if a categorical variable produces good segments within a scatter plot? [closed]
In cluster analysis, the Silhouette coefficient (SC; or Average Silhouette Width) is a distance-based statistic that measures the quality of a clustering, i.e., to what extent the objects are closer to other... | Are there any statistics to see if a categorical variable produces good segments within a scatter pl
In cluster analysis, the Silhouette coefficient (SC; or Average Silhouette Width) is a distance-based statistic that measures the quality of a clustering, i.e., to what extent the objects are closer t |
44,021 | Are there any statistics to see if a categorical variable produces good segments within a scatter plot? [closed] | This is an interesting and large question and no answer is likely to seem complete.
You can take the question further graphically and you can take it further numerically. Existing methods do help and so I see little or no call to invent methods ad hoc.
Graphics
Your first plot already includes ellipses fitted somehow a... | Are there any statistics to see if a categorical variable produces good segments within a scatter pl | This is an interesting and large question and no answer is likely to seem complete.
You can take the question further graphically and you can take it further numerically. Existing methods do help and | Are there any statistics to see if a categorical variable produces good segments within a scatter plot? [closed]
This is an interesting and large question and no answer is likely to seem complete.
You can take the question further graphically and you can take it further numerically. Existing methods do help and so I se... | Are there any statistics to see if a categorical variable produces good segments within a scatter pl
This is an interesting and large question and no answer is likely to seem complete.
You can take the question further graphically and you can take it further numerically. Existing methods do help and |
44,022 | Are there any statistics to see if a categorical variable produces good segments within a scatter plot? [closed] | You need two steps:
Some way of modelling the distribution of the different categories
Comparing the distributions of the different categories.
There are many different ways to model distributions and to compare the difference between distributions.
A classical example would be MANOVA which models the mean and covari... | Are there any statistics to see if a categorical variable produces good segments within a scatter pl | You need two steps:
Some way of modelling the distribution of the different categories
Comparing the distributions of the different categories.
There are many different ways to model distributions a | Are there any statistics to see if a categorical variable produces good segments within a scatter plot? [closed]
You need two steps:
Some way of modelling the distribution of the different categories
Comparing the distributions of the different categories.
There are many different ways to model distributions and to c... | Are there any statistics to see if a categorical variable produces good segments within a scatter pl
You need two steps:
Some way of modelling the distribution of the different categories
Comparing the distributions of the different categories.
There are many different ways to model distributions a |
44,023 | Are there any statistics to see if a categorical variable produces good segments within a scatter plot? [closed] | I interpreted
visually well-defined segments
as "separable in some (natural) space parametrization". I assume that for you, this is not a case of visually well-defined segments:
Further, your first image seems to suggest a GMM-type geometry, which is a natural choice. Since you already know the categorical part of y... | Are there any statistics to see if a categorical variable produces good segments within a scatter pl | I interpreted
visually well-defined segments
as "separable in some (natural) space parametrization". I assume that for you, this is not a case of visually well-defined segments:
Further, your first | Are there any statistics to see if a categorical variable produces good segments within a scatter plot? [closed]
I interpreted
visually well-defined segments
as "separable in some (natural) space parametrization". I assume that for you, this is not a case of visually well-defined segments:
Further, your first image ... | Are there any statistics to see if a categorical variable produces good segments within a scatter pl
I interpreted
visually well-defined segments
as "separable in some (natural) space parametrization". I assume that for you, this is not a case of visually well-defined segments:
Further, your first |
44,024 | Intuition behind a 0% central/equal-tailed confidence interval | You are close
For a continuous distribution, the 0% equal-tail CI occurs at the point corresponding to the median of the true distribution of the pivotal quantity that is used in constructing the CI. It is not always possible to invert the pivotal quantity in a way that yields an unbiased estimator of a corresponding ... | Intuition behind a 0% central/equal-tailed confidence interval | You are close
For a continuous distribution, the 0% equal-tail CI occurs at the point corresponding to the median of the true distribution of the pivotal quantity that is used in constructing the CI. | Intuition behind a 0% central/equal-tailed confidence interval
You are close
For a continuous distribution, the 0% equal-tail CI occurs at the point corresponding to the median of the true distribution of the pivotal quantity that is used in constructing the CI. It is not always possible to invert the pivotal quantity... | Intuition behind a 0% central/equal-tailed confidence interval
You are close
For a continuous distribution, the 0% equal-tail CI occurs at the point corresponding to the median of the true distribution of the pivotal quantity that is used in constructing the CI. |
44,025 | Intuition behind a 0% central/equal-tailed confidence interval | A frequentist 0% confidence interval can be any point within the parameter space. One might prefer to choose a point that is near to the maximum likelihood estimate, but any other point will be just as validly a 0% confidence interval.
Typical 95% confidence intervals are usually at least roughly centred around the max... | Intuition behind a 0% central/equal-tailed confidence interval | A frequentist 0% confidence interval can be any point within the parameter space. One might prefer to choose a point that is near to the maximum likelihood estimate, but any other point will be just a | Intuition behind a 0% central/equal-tailed confidence interval
A frequentist 0% confidence interval can be any point within the parameter space. One might prefer to choose a point that is near to the maximum likelihood estimate, but any other point will be just as validly a 0% confidence interval.
Typical 95% confidenc... | Intuition behind a 0% central/equal-tailed confidence interval
A frequentist 0% confidence interval can be any point within the parameter space. One might prefer to choose a point that is near to the maximum likelihood estimate, but any other point will be just a |
44,026 | Intuition behind a 0% central/equal-tailed confidence interval | A zero-level confidence interval can be seen as an estimator. Indeed, it has been advocated by Skovgaard (1989) "A review of higher-order likelihood inference". Bull. Int. Statist. Inst., 53, 331–351, in a class of two-sided equal-tailed confidence intervals, and is defined as the intersection of all confidence interva... | Intuition behind a 0% central/equal-tailed confidence interval | A zero-level confidence interval can be seen as an estimator. Indeed, it has been advocated by Skovgaard (1989) "A review of higher-order likelihood inference". Bull. Int. Statist. Inst., 53, 331–351, | Intuition behind a 0% central/equal-tailed confidence interval
A zero-level confidence interval can be seen as an estimator. Indeed, it has been advocated by Skovgaard (1989) "A review of higher-order likelihood inference". Bull. Int. Statist. Inst., 53, 331–351, in a class of two-sided equal-tailed confidence interval... | Intuition behind a 0% central/equal-tailed confidence interval
A zero-level confidence interval can be seen as an estimator. Indeed, it has been advocated by Skovgaard (1989) "A review of higher-order likelihood inference". Bull. Int. Statist. Inst., 53, 331–351, |
44,027 | Categorical or Categorial? Is there a difference between the two terms from a statistician's point of view? | I have literally never heard 'categorial' (without the second C) and assumed that this was a typo. But some googling does indicate that this word is used - in linguistics.
In statistics, as far as I know, we only use categorical.
As mild support for this claim, if one googles 'categorial statistics', Google assumes you... | Categorical or Categorial? Is there a difference between the two terms from a statistician's point o | I have literally never heard 'categorial' (without the second C) and assumed that this was a typo. But some googling does indicate that this word is used - in linguistics.
In statistics, as far as I k | Categorical or Categorial? Is there a difference between the two terms from a statistician's point of view?
I have literally never heard 'categorial' (without the second C) and assumed that this was a typo. But some googling does indicate that this word is used - in linguistics.
In statistics, as far as I know, we only... | Categorical or Categorial? Is there a difference between the two terms from a statistician's point o
I have literally never heard 'categorial' (without the second C) and assumed that this was a typo. But some googling does indicate that this word is used - in linguistics.
In statistics, as far as I k |
44,028 | Categorical or Categorial? Is there a difference between the two terms from a statistician's point of view? | I second mkt's answer; this is a long comment rather than an answer. In math I've never seen "Categorial".
Also, I think in English that word is rarely used. The frequency is about 20 times less than the proper word "categorical"
I asked a question here and English experts will surely help us. | Categorical or Categorial? Is there a difference between the two terms from a statistician's point o | I second mkt's answer; this is a long comment rather than an answer. In math I've never seen "Categorial".
Also, I think in English that word is rarely used. The frequency is about 20 times less than | Categorical or Categorial? Is there a difference between the two terms from a statistician's point of view?
I second mkt's answer; this is a long comment rather than an answer. In math I've never seen "Categorial".
Also, I think in English that word is rarely used. The frequency is about 20 times less than the proper w... | Categorical or Categorial? Is there a difference between the two terms from a statistician's point o
I second mkt's answer; this is a long comment rather than an answer. In math I've never seen "Categorial".
Also, I think in English that word is rarely used. The frequency is about 20 times less than |
44,029 | Model Selection: AIC/BIC and Cross-Validation gives different conclusion | Nothing strange in here.
If all the model selection methods always gave the same results, we wouldn't have multiple criteria, but just pick arbitrary one.
AIC and BIC explicitly penalize the number of parameters, cross-validation not, so again, it's not surprising that they suggest a model with fewer parameters (thoug... | Model Selection: AIC/BIC and Cross-Validation gives different conclusion | Nothing strange in here.
If all the model selection methods always gave the same results, we wouldn't have multiple criteria, but just pick arbitrary one.
AIC and BIC explicitly penalize the number o | Model Selection: AIC/BIC and Cross-Validation gives different conclusion
Nothing strange in here.
If all the model selection methods always gave the same results, we wouldn't have multiple criteria, but just pick arbitrary one.
AIC and BIC explicitly penalize the number of parameters, cross-validation not, so again, i... | Model Selection: AIC/BIC and Cross-Validation gives different conclusion
Nothing strange in here.
If all the model selection methods always gave the same results, we wouldn't have multiple criteria, but just pick arbitrary one.
AIC and BIC explicitly penalize the number o |
44,030 | Model Selection: AIC/BIC and Cross-Validation gives different conclusion | AIC is asymptotically equivalent to leave-1-out cross-validation (LOOCV)
It's not equivalent to 10-fold cross-validation, which is what you're comparing it to.
It's only asymptotically equivalent, so the two methods don't always give the same answer, they're only approximately the same.
It's not really clear how you'... | Model Selection: AIC/BIC and Cross-Validation gives different conclusion | AIC is asymptotically equivalent to leave-1-out cross-validation (LOOCV)
It's not equivalent to 10-fold cross-validation, which is what you're comparing it to.
It's only asymptotically equivalent, so | Model Selection: AIC/BIC and Cross-Validation gives different conclusion
AIC is asymptotically equivalent to leave-1-out cross-validation (LOOCV)
It's not equivalent to 10-fold cross-validation, which is what you're comparing it to.
It's only asymptotically equivalent, so the two methods don't always give the same ans... | Model Selection: AIC/BIC and Cross-Validation gives different conclusion
AIC is asymptotically equivalent to leave-1-out cross-validation (LOOCV)
It's not equivalent to 10-fold cross-validation, which is what you're comparing it to.
It's only asymptotically equivalent, so |
44,031 | Model Selection: AIC/BIC and Cross-Validation gives different conclusion | Maybe you should concentrate more on the methods that are intended precisely for feature selection, rather than model selection. Model selection methods like cross-validation or AIC try to compare models independently of how they differ (this is only approximately true, but should suffice here). Feature selection metho... | Model Selection: AIC/BIC and Cross-Validation gives different conclusion | Maybe you should concentrate more on the methods that are intended precisely for feature selection, rather than model selection. Model selection methods like cross-validation or AIC try to compare mod | Model Selection: AIC/BIC and Cross-Validation gives different conclusion
Maybe you should concentrate more on the methods that are intended precisely for feature selection, rather than model selection. Model selection methods like cross-validation or AIC try to compare models independently of how they differ (this is o... | Model Selection: AIC/BIC and Cross-Validation gives different conclusion
Maybe you should concentrate more on the methods that are intended precisely for feature selection, rather than model selection. Model selection methods like cross-validation or AIC try to compare mod |
44,032 | What does it mean when a Data Matrix has full rank? | If the matrix has full rank, i.e. $rank(M) = p$ and $n > p$, the p variables are linearly independent and therefore there is no redundancy in the data. If instead the $rank(M) < p$ some columns can be recreated by linearly combining the others. In this latter case, you couldn't use all the columns of M as explanatory v... | What does it mean when a Data Matrix has full rank? | If the matrix has full rank, i.e. $rank(M) = p$ and $n > p$, the p variables are linearly independent and therefore there is no redundancy in the data. If instead the $rank(M) < p$ some columns can be | What does it mean when a Data Matrix has full rank?
If the matrix has full rank, i.e. $rank(M) = p$ and $n > p$, the p variables are linearly independent and therefore there is no redundancy in the data. If instead the $rank(M) < p$ some columns can be recreated by linearly combining the others. In this latter case, yo... | What does it mean when a Data Matrix has full rank?
If the matrix has full rank, i.e. $rank(M) = p$ and $n > p$, the p variables are linearly independent and therefore there is no redundancy in the data. If instead the $rank(M) < p$ some columns can be |
44,033 | What does it mean when a Data Matrix has full rank? | I want to connect the concept of identifiability with the rank of the design matrix in linear regression, as well as take a more linear algebraic look at the problem, since you mention you have a math background.
Some parameter in our regression model is called identifiable if it's possible for us to even guess what it... | What does it mean when a Data Matrix has full rank? | I want to connect the concept of identifiability with the rank of the design matrix in linear regression, as well as take a more linear algebraic look at the problem, since you mention you have a math | What does it mean when a Data Matrix has full rank?
I want to connect the concept of identifiability with the rank of the design matrix in linear regression, as well as take a more linear algebraic look at the problem, since you mention you have a math background.
Some parameter in our regression model is called identi... | What does it mean when a Data Matrix has full rank?
I want to connect the concept of identifiability with the rank of the design matrix in linear regression, as well as take a more linear algebraic look at the problem, since you mention you have a math |
44,034 | What does it mean when a Data Matrix has full rank? | Suppose you have a $10×10$ matrix $X$, and $rank(X) = 1$, which is stored somewhere on your memory disk. For some reason, your memory disk was damaged, and so was the matrix. Some rows remained intact, whereas in other rows several digits were lost. The question is, how many full rows do you need to know in order to re... | What does it mean when a Data Matrix has full rank? | Suppose you have a $10×10$ matrix $X$, and $rank(X) = 1$, which is stored somewhere on your memory disk. For some reason, your memory disk was damaged, and so was the matrix. Some rows remained intact | What does it mean when a Data Matrix has full rank?
Suppose you have a $10×10$ matrix $X$, and $rank(X) = 1$, which is stored somewhere on your memory disk. For some reason, your memory disk was damaged, and so was the matrix. Some rows remained intact, whereas in other rows several digits were lost. The question is, h... | What does it mean when a Data Matrix has full rank?
Suppose you have a $10×10$ matrix $X$, and $rank(X) = 1$, which is stored somewhere on your memory disk. For some reason, your memory disk was damaged, and so was the matrix. Some rows remained intact |
44,035 | Expectation Maximization and Deep Learning | (The original version of this post, the text of which I kept below the line for reference purposes, generated a lot of dispute and some back and forth which seems mostly to be around questions of interpretation and ambiguity, so I updated with a more direct answer)
The OP seems to be asking:
Are Deep Learning models... | Expectation Maximization and Deep Learning | (The original version of this post, the text of which I kept below the line for reference purposes, generated a lot of dispute and some back and forth which seems mostly to be around questions of inte | Expectation Maximization and Deep Learning
(The original version of this post, the text of which I kept below the line for reference purposes, generated a lot of dispute and some back and forth which seems mostly to be around questions of interpretation and ambiguity, so I updated with a more direct answer)
The OP see... | Expectation Maximization and Deep Learning
(The original version of this post, the text of which I kept below the line for reference purposes, generated a lot of dispute and some back and forth which seems mostly to be around questions of inte |
44,036 | Expectation Maximization and Deep Learning | In short, no.
Expectation maximization is a technique to solve statistical problems that consist of an "easy" maximization (if some latent variables were known), and an "easy" expectation calculation on the log-likelihood (if the parameters were known). However, the "how" and "why" the expectation and maximization ste... | Expectation Maximization and Deep Learning | In short, no.
Expectation maximization is a technique to solve statistical problems that consist of an "easy" maximization (if some latent variables were known), and an "easy" expectation calculation | Expectation Maximization and Deep Learning
In short, no.
Expectation maximization is a technique to solve statistical problems that consist of an "easy" maximization (if some latent variables were known), and an "easy" expectation calculation on the log-likelihood (if the parameters were known). However, the "how" and... | Expectation Maximization and Deep Learning
In short, no.
Expectation maximization is a technique to solve statistical problems that consist of an "easy" maximization (if some latent variables were known), and an "easy" expectation calculation |
44,037 | Expectation Maximization and Deep Learning | Short overview about Expectation maximization :
Marginal likelihood
Expectation maximization contrasts with 'regular' likelihood maximization by refering to the maximization of a marginal likelihood.
$$\underbrace{p(X\vert \theta)}_{\substack{\text{marginal likelihood}\\\text{ $\mathcal{L}(\theta \vert X)$}}} =
\int_... | Expectation Maximization and Deep Learning | Short overview about Expectation maximization :
Marginal likelihood
Expectation maximization contrasts with 'regular' likelihood maximization by refering to the maximization of a marginal likelihood. | Expectation Maximization and Deep Learning
Short overview about Expectation maximization :
Marginal likelihood
Expectation maximization contrasts with 'regular' likelihood maximization by refering to the maximization of a marginal likelihood.
$$\underbrace{p(X\vert \theta)}_{\substack{\text{marginal likelihood}\\\text... | Expectation Maximization and Deep Learning
Short overview about Expectation maximization :
Marginal likelihood
Expectation maximization contrasts with 'regular' likelihood maximization by refering to the maximization of a marginal likelihood. |
44,038 | Expectation Maximization and Deep Learning | Given the previous technical answers, one philosophical point may help to clear the ambiguity between ME vs. Deep Learning: the concept of learning.
In deep learning, there are multiple layers, each layer is a learning step. In the first step, the data input is 'converted' (or learned) into a synthetic intermediate out... | Expectation Maximization and Deep Learning | Given the previous technical answers, one philosophical point may help to clear the ambiguity between ME vs. Deep Learning: the concept of learning.
In deep learning, there are multiple layers, each l | Expectation Maximization and Deep Learning
Given the previous technical answers, one philosophical point may help to clear the ambiguity between ME vs. Deep Learning: the concept of learning.
In deep learning, there are multiple layers, each layer is a learning step. In the first step, the data input is 'converted' (or... | Expectation Maximization and Deep Learning
Given the previous technical answers, one philosophical point may help to clear the ambiguity between ME vs. Deep Learning: the concept of learning.
In deep learning, there are multiple layers, each l |
44,039 | In R, how do I test $H_0: \beta_1+\beta_2=0$ | If $\beta_1 + \beta_2 = 0$, then $\beta_1 = -\beta_2$, so $\beta_1 x_t + \beta_2 z_t = \beta_1 x_t - \beta_1 z_t = \beta_1 (x_t - z_t)$. So, in R, you can run
f1 <- lm(y ~ I(x - z), data = data)
f2 <- lm(y ~ x + z, data = data)
anova(f1, f2)
which will give you a test if the model where $\beta_1 + \beta_2 = 0$ (i.e.,... | In R, how do I test $H_0: \beta_1+\beta_2=0$ | If $\beta_1 + \beta_2 = 0$, then $\beta_1 = -\beta_2$, so $\beta_1 x_t + \beta_2 z_t = \beta_1 x_t - \beta_1 z_t = \beta_1 (x_t - z_t)$. So, in R, you can run
f1 <- lm(y ~ I(x - z), data = data)
f2 < | In R, how do I test $H_0: \beta_1+\beta_2=0$
If $\beta_1 + \beta_2 = 0$, then $\beta_1 = -\beta_2$, so $\beta_1 x_t + \beta_2 z_t = \beta_1 x_t - \beta_1 z_t = \beta_1 (x_t - z_t)$. So, in R, you can run
f1 <- lm(y ~ I(x - z), data = data)
f2 <- lm(y ~ x + z, data = data)
anova(f1, f2)
which will give you a test if t... | In R, how do I test $H_0: \beta_1+\beta_2=0$
If $\beta_1 + \beta_2 = 0$, then $\beta_1 = -\beta_2$, so $\beta_1 x_t + \beta_2 z_t = \beta_1 x_t - \beta_1 z_t = \beta_1 (x_t - z_t)$. So, in R, you can run
f1 <- lm(y ~ I(x - z), data = data)
f2 < |
44,040 | In R, how do I test $H_0: \beta_1+\beta_2=0$ | Great thread which generated some great answers - though I have a feeling it will be moved to Stack Exchange because it is software related (the software being R).
To supplement Noah's answer, I will show an alternative way one can test the hypotheses of interest using the multcomp package. [Note that we can't test ... | In R, how do I test $H_0: \beta_1+\beta_2=0$ | Great thread which generated some great answers - though I have a feeling it will be moved to Stack Exchange because it is software related (the software being R).
To supplement Noah's answer, I wil | In R, how do I test $H_0: \beta_1+\beta_2=0$
Great thread which generated some great answers - though I have a feeling it will be moved to Stack Exchange because it is software related (the software being R).
To supplement Noah's answer, I will show an alternative way one can test the hypotheses of interest using the... | In R, how do I test $H_0: \beta_1+\beta_2=0$
Great thread which generated some great answers - though I have a feeling it will be moved to Stack Exchange because it is software related (the software being R).
To supplement Noah's answer, I wil |
44,041 | In R, how do I test $H_0: \beta_1+\beta_2=0$ | The variance of $\beta_1 + \beta_2$ is $\operatorname{Var}(\beta_1) + \operatorname{Var}(\beta_2)
+ 2\operatorname{Cov}(\beta_1,\beta_2)$. Obtain the variance and covaraince from the covariance matrix and construct an appropriate confidence interval.
Here is some R code. I'm sure there is a package to do this, but un... | In R, how do I test $H_0: \beta_1+\beta_2=0$ | The variance of $\beta_1 + \beta_2$ is $\operatorname{Var}(\beta_1) + \operatorname{Var}(\beta_2)
+ 2\operatorname{Cov}(\beta_1,\beta_2)$. Obtain the variance and covaraince from the covariance matri | In R, how do I test $H_0: \beta_1+\beta_2=0$
The variance of $\beta_1 + \beta_2$ is $\operatorname{Var}(\beta_1) + \operatorname{Var}(\beta_2)
+ 2\operatorname{Cov}(\beta_1,\beta_2)$. Obtain the variance and covaraince from the covariance matrix and construct an appropriate confidence interval.
Here is some R code. I... | In R, how do I test $H_0: \beta_1+\beta_2=0$
The variance of $\beta_1 + \beta_2$ is $\operatorname{Var}(\beta_1) + \operatorname{Var}(\beta_2)
+ 2\operatorname{Cov}(\beta_1,\beta_2)$. Obtain the variance and covaraince from the covariance matri |
44,042 | In R, how do I test $H_0: \beta_1+\beta_2=0$ | Maybe you can try a Chi-square goodness of fit ( observed -expected) for your data points, for two models, one with $\beta_1= -\beta_2$ and another one where you use $\beta_2 \in (\beta_1 - \epsilon, \beta_1 + \epsilon )$ with your choice of $\epsilon >0 $ a Real number, and check the two Chi-squared statistics using ... | In R, how do I test $H_0: \beta_1+\beta_2=0$ | Maybe you can try a Chi-square goodness of fit ( observed -expected) for your data points, for two models, one with $\beta_1= -\beta_2$ and another one where you use $\beta_2 \in (\beta_1 - \epsilon, | In R, how do I test $H_0: \beta_1+\beta_2=0$
Maybe you can try a Chi-square goodness of fit ( observed -expected) for your data points, for two models, one with $\beta_1= -\beta_2$ and another one where you use $\beta_2 \in (\beta_1 - \epsilon, \beta_1 + \epsilon )$ with your choice of $\epsilon >0 $ a Real number, an... | In R, how do I test $H_0: \beta_1+\beta_2=0$
Maybe you can try a Chi-square goodness of fit ( observed -expected) for your data points, for two models, one with $\beta_1= -\beta_2$ and another one where you use $\beta_2 \in (\beta_1 - \epsilon, |
44,043 | How do I generate distribution of positive numbers only with min, max and mean? | While the problem is very much ill-posed, since there is an infinite range of distributions satisfying these constraints, a possible solution is to find the maximum entropy distribution under the constraint of a support of $(80,12000)$ [thus using the uniform measure on that interval as the reference measure] and a mea... | How do I generate distribution of positive numbers only with min, max and mean? | While the problem is very much ill-posed, since there is an infinite range of distributions satisfying these constraints, a possible solution is to find the maximum entropy distribution under the cons | How do I generate distribution of positive numbers only with min, max and mean?
While the problem is very much ill-posed, since there is an infinite range of distributions satisfying these constraints, a possible solution is to find the maximum entropy distribution under the constraint of a support of $(80,12000)$ [thu... | How do I generate distribution of positive numbers only with min, max and mean?
While the problem is very much ill-posed, since there is an infinite range of distributions satisfying these constraints, a possible solution is to find the maximum entropy distribution under the cons |
44,044 | How do I generate distribution of positive numbers only with min, max and mean? | If you don't care about the distribution aside from min, max, and mean, then there is a simple answer.
Take 96.476510067114100 percent of draws as 80 and 3.523489932885910 percent of draws as 12000. On average, you get 500, and you have your min and max. I calculated the percentages by solving a system of equations
$$... | How do I generate distribution of positive numbers only with min, max and mean? | If you don't care about the distribution aside from min, max, and mean, then there is a simple answer.
Take 96.476510067114100 percent of draws as 80 and 3.523489932885910 percent of draws as 12000. O | How do I generate distribution of positive numbers only with min, max and mean?
If you don't care about the distribution aside from min, max, and mean, then there is a simple answer.
Take 96.476510067114100 percent of draws as 80 and 3.523489932885910 percent of draws as 12000. On average, you get 500, and you have you... | How do I generate distribution of positive numbers only with min, max and mean?
If you don't care about the distribution aside from min, max, and mean, then there is a simple answer.
Take 96.476510067114100 percent of draws as 80 and 3.523489932885910 percent of draws as 12000. O |
44,045 | How do I generate distribution of positive numbers only with min, max and mean? | Use for example a beta distribution, shifted and rescaled to your min and max.
The beta is easy to use here since it is bounded to the interval [0;1], but the mean can be placed by parameterization.
You have mean=alpha/(alpha+beta) and hence beta=alpha/mean - alpha, or in the rescaled version beta=alpha*(max-min)/(mean... | How do I generate distribution of positive numbers only with min, max and mean? | Use for example a beta distribution, shifted and rescaled to your min and max.
The beta is easy to use here since it is bounded to the interval [0;1], but the mean can be placed by parameterization.
Y | How do I generate distribution of positive numbers only with min, max and mean?
Use for example a beta distribution, shifted and rescaled to your min and max.
The beta is easy to use here since it is bounded to the interval [0;1], but the mean can be placed by parameterization.
You have mean=alpha/(alpha+beta) and henc... | How do I generate distribution of positive numbers only with min, max and mean?
Use for example a beta distribution, shifted and rescaled to your min and max.
The beta is easy to use here since it is bounded to the interval [0;1], but the mean can be placed by parameterization.
Y |
44,046 | Sum of normal independent random variables with coefficients | First, let me note there is nothing special in having the coefficients
of the linear combination to be less or more than one.
The moment generating function is defined as
$$M_X(s)=\mathbb{E}[\exp\{sX\}]$$
when this expectation exists. Considering a linear combination of independent random variables, like $2X+3Y$, le... | Sum of normal independent random variables with coefficients | First, let me note there is nothing special in having the coefficients
of the linear combination to be less or more than one.
The moment generating function is defined as
$$M_X(s)=\mathbb{E}[\exp\{ | Sum of normal independent random variables with coefficients
First, let me note there is nothing special in having the coefficients
of the linear combination to be less or more than one.
The moment generating function is defined as
$$M_X(s)=\mathbb{E}[\exp\{sX\}]$$
when this expectation exists. Considering a linear ... | Sum of normal independent random variables with coefficients
First, let me note there is nothing special in having the coefficients
of the linear combination to be less or more than one.
The moment generating function is defined as
$$M_X(s)=\mathbb{E}[\exp\{ |
44,047 | Sum of normal independent random variables with coefficients | Use MGF to determine that a linear combination of normal random variables is normal. (MGF uniquely defines the distribution)
Since the sum of normals is normal, take the expectation and variance of
$2X + 3Y$ to find the parameters governing the normal distribution. Use the property of variance: $V(\sum X_i) = \sum V(X... | Sum of normal independent random variables with coefficients | Use MGF to determine that a linear combination of normal random variables is normal. (MGF uniquely defines the distribution)
Since the sum of normals is normal, take the expectation and variance of
$ | Sum of normal independent random variables with coefficients
Use MGF to determine that a linear combination of normal random variables is normal. (MGF uniquely defines the distribution)
Since the sum of normals is normal, take the expectation and variance of
$2X + 3Y$ to find the parameters governing the normal distri... | Sum of normal independent random variables with coefficients
Use MGF to determine that a linear combination of normal random variables is normal. (MGF uniquely defines the distribution)
Since the sum of normals is normal, take the expectation and variance of
$ |
44,048 | Sum of normal independent random variables with coefficients | You don't need to use moment generating functions. The sum of two independent normal random variables is normal with mean equal to the sum of the means and the variance equal to the sum of the variances. Also a constant c times a normal random variable is normal with mean c$\mu$ where $\mu$ is the mean of the original ... | Sum of normal independent random variables with coefficients | You don't need to use moment generating functions. The sum of two independent normal random variables is normal with mean equal to the sum of the means and the variance equal to the sum of the varianc | Sum of normal independent random variables with coefficients
You don't need to use moment generating functions. The sum of two independent normal random variables is normal with mean equal to the sum of the means and the variance equal to the sum of the variances. Also a constant c times a normal random variable is nor... | Sum of normal independent random variables with coefficients
You don't need to use moment generating functions. The sum of two independent normal random variables is normal with mean equal to the sum of the means and the variance equal to the sum of the varianc |
44,049 | What to conclude when most results are statistically significant to fail to reject null hypothesis but not all? | If all of your null hypothesis are, in reality, true, then your probability of rejecting in at least one of your experiments is
$$ 1 - 0.95^4 \approx 0.19 $$
So there about a 20% chance you would find at least one rejection in your experiment, even if all of the bags had an equal distribution of colors. Not too unlike... | What to conclude when most results are statistically significant to fail to reject null hypothesis b | If all of your null hypothesis are, in reality, true, then your probability of rejecting in at least one of your experiments is
$$ 1 - 0.95^4 \approx 0.19 $$
So there about a 20% chance you would find | What to conclude when most results are statistically significant to fail to reject null hypothesis but not all?
If all of your null hypothesis are, in reality, true, then your probability of rejecting in at least one of your experiments is
$$ 1 - 0.95^4 \approx 0.19 $$
So there about a 20% chance you would find at leas... | What to conclude when most results are statistically significant to fail to reject null hypothesis b
If all of your null hypothesis are, in reality, true, then your probability of rejecting in at least one of your experiments is
$$ 1 - 0.95^4 \approx 0.19 $$
So there about a 20% chance you would find |
44,050 | What to conclude when most results are statistically significant to fail to reject null hypothesis but not all? | If you are trying to test if distribution depends on the bag -or, equivalently, if all bags are random samples from the same population- performing tests on pairs of bags is not going to work, because it can yield contradictory results -as you found- and because probability of type I errors is going to build up due to ... | What to conclude when most results are statistically significant to fail to reject null hypothesis b | If you are trying to test if distribution depends on the bag -or, equivalently, if all bags are random samples from the same population- performing tests on pairs of bags is not going to work, because | What to conclude when most results are statistically significant to fail to reject null hypothesis but not all?
If you are trying to test if distribution depends on the bag -or, equivalently, if all bags are random samples from the same population- performing tests on pairs of bags is not going to work, because it can ... | What to conclude when most results are statistically significant to fail to reject null hypothesis b
If you are trying to test if distribution depends on the bag -or, equivalently, if all bags are random samples from the same population- performing tests on pairs of bags is not going to work, because |
44,051 | What to conclude when most results are statistically significant to fail to reject null hypothesis but not all? | After you explain the results in the Results chapter, you can state in the discussion that one result was found significant. You can provide your interpretation of the results based on literature and suggest number of plausbile explanations to the reader. | What to conclude when most results are statistically significant to fail to reject null hypothesis b | After you explain the results in the Results chapter, you can state in the discussion that one result was found significant. You can provide your interpretation of the results based on literature and | What to conclude when most results are statistically significant to fail to reject null hypothesis but not all?
After you explain the results in the Results chapter, you can state in the discussion that one result was found significant. You can provide your interpretation of the results based on literature and suggest ... | What to conclude when most results are statistically significant to fail to reject null hypothesis b
After you explain the results in the Results chapter, you can state in the discussion that one result was found significant. You can provide your interpretation of the results based on literature and |
44,052 | How to calculate mean and standard deviation from median and quartiles | You can check Wan et al. (2014)*. They build on Bland (2014) to estimate these parameters according to the data summaries available. See scenario C3 in their paper :
$$ \bar{X} ≈ \frac {q_{1} + m + q_{3}}{3}$$
$$ S ≈ \frac {q_{3} - q_{1}}{1.35}$$
or, if you have the sample size :
$$ S ≈ \frac {q_{3} - q_{1}}{2 \Phi^{-1... | How to calculate mean and standard deviation from median and quartiles | You can check Wan et al. (2014)*. They build on Bland (2014) to estimate these parameters according to the data summaries available. See scenario C3 in their paper :
$$ \bar{X} ≈ \frac {q_{1} + m + q_ | How to calculate mean and standard deviation from median and quartiles
You can check Wan et al. (2014)*. They build on Bland (2014) to estimate these parameters according to the data summaries available. See scenario C3 in their paper :
$$ \bar{X} ≈ \frac {q_{1} + m + q_{3}}{3}$$
$$ S ≈ \frac {q_{3} - q_{1}}{1.35}$$
or... | How to calculate mean and standard deviation from median and quartiles
You can check Wan et al. (2014)*. They build on Bland (2014) to estimate these parameters according to the data summaries available. See scenario C3 in their paper :
$$ \bar{X} ≈ \frac {q_{1} + m + q_ |
44,053 | How to calculate mean and standard deviation from median and quartiles | Adding to Michael Chernick's comment, here's an example.
x <- runif(1000,0,1)
summary(x) #1st Q = 0.27 3rd = 0.77 mean = .51
x1 <- c(x,100)
summary(x1) #1Q = 0.27 3rd = 0.77 mean = .61
x2 <- c(rnorm(100,0,1), rnorm(10,10,.1))
summary(x2) # 1st = -.85 3rd = 0.69, mean = 0.71
With the first pair, note that a si... | How to calculate mean and standard deviation from median and quartiles | Adding to Michael Chernick's comment, here's an example.
x <- runif(1000,0,1)
summary(x) #1st Q = 0.27 3rd = 0.77 mean = .51
x1 <- c(x,100)
summary(x1) #1Q = 0.27 3rd = 0.77 mean = .61
x2 <- c( | How to calculate mean and standard deviation from median and quartiles
Adding to Michael Chernick's comment, here's an example.
x <- runif(1000,0,1)
summary(x) #1st Q = 0.27 3rd = 0.77 mean = .51
x1 <- c(x,100)
summary(x1) #1Q = 0.27 3rd = 0.77 mean = .61
x2 <- c(rnorm(100,0,1), rnorm(10,10,.1))
summary(x2) # 1... | How to calculate mean and standard deviation from median and quartiles
Adding to Michael Chernick's comment, here's an example.
x <- runif(1000,0,1)
summary(x) #1st Q = 0.27 3rd = 0.77 mean = .51
x1 <- c(x,100)
summary(x1) #1Q = 0.27 3rd = 0.77 mean = .61
x2 <- c( |
44,054 | How to calculate mean and standard deviation from median and quartiles | There is a detailed publication on this topic from Greco et al, How to impute study-specific standard deviations in meta-analyses of skewed continuous endpoints? World Journal of Meta-Analysis 2015;3(5):215-224.
The main findings of this work are that it is acceptable to approximate "missing values of mean and SD with ... | How to calculate mean and standard deviation from median and quartiles | There is a detailed publication on this topic from Greco et al, How to impute study-specific standard deviations in meta-analyses of skewed continuous endpoints? World Journal of Meta-Analysis 2015;3( | How to calculate mean and standard deviation from median and quartiles
There is a detailed publication on this topic from Greco et al, How to impute study-specific standard deviations in meta-analyses of skewed continuous endpoints? World Journal of Meta-Analysis 2015;3(5):215-224.
The main findings of this work are th... | How to calculate mean and standard deviation from median and quartiles
There is a detailed publication on this topic from Greco et al, How to impute study-specific standard deviations in meta-analyses of skewed continuous endpoints? World Journal of Meta-Analysis 2015;3( |
44,055 | How to calculate mean and standard deviation from median and quartiles | If you know that the data is normally distributed, you can infer it given the lower and upper quantiles.
norm_from_quantiles = function(lower, upper, p = 0.25) {
mu = mean(c(lower, upper))
sigma = (lower - mu) / qnorm(p)
list(mu = mu, sigma = sigma)
}
Here, p and 1-p are the quantiles of lower and upper so p = 0... | How to calculate mean and standard deviation from median and quartiles | If you know that the data is normally distributed, you can infer it given the lower and upper quantiles.
norm_from_quantiles = function(lower, upper, p = 0.25) {
mu = mean(c(lower, upper))
sigma = | How to calculate mean and standard deviation from median and quartiles
If you know that the data is normally distributed, you can infer it given the lower and upper quantiles.
norm_from_quantiles = function(lower, upper, p = 0.25) {
mu = mean(c(lower, upper))
sigma = (lower - mu) / qnorm(p)
list(mu = mu, sigma = ... | How to calculate mean and standard deviation from median and quartiles
If you know that the data is normally distributed, you can infer it given the lower and upper quantiles.
norm_from_quantiles = function(lower, upper, p = 0.25) {
mu = mean(c(lower, upper))
sigma = |
44,056 | How to calculate mean and standard deviation from median and quartiles | I faced similar problem , where i calculated percentiles (0 to 100%) and then I was asked to give back mean as well , after playing in my notebook i noticed that the empirical mean of the quantiles list is in fact the mean of the distribution , thought i discovered a new theorem hahah but then found this
https://en.wik... | How to calculate mean and standard deviation from median and quartiles | I faced similar problem , where i calculated percentiles (0 to 100%) and then I was asked to give back mean as well , after playing in my notebook i noticed that the empirical mean of the quantiles li | How to calculate mean and standard deviation from median and quartiles
I faced similar problem , where i calculated percentiles (0 to 100%) and then I was asked to give back mean as well , after playing in my notebook i noticed that the empirical mean of the quantiles list is in fact the mean of the distribution , thou... | How to calculate mean and standard deviation from median and quartiles
I faced similar problem , where i calculated percentiles (0 to 100%) and then I was asked to give back mean as well , after playing in my notebook i noticed that the empirical mean of the quantiles li |
44,057 | Relationship between RMSE and RSS | Having the mathematical derivations, you might ask yourself why use one measure over the other to assess the performance of a given model? You could use either, but the advantage of RMSE is that it will come out in more interpretable units. For example, if you were building a model that used house features to predict h... | Relationship between RMSE and RSS | Having the mathematical derivations, you might ask yourself why use one measure over the other to assess the performance of a given model? You could use either, but the advantage of RMSE is that it wi | Relationship between RMSE and RSS
Having the mathematical derivations, you might ask yourself why use one measure over the other to assess the performance of a given model? You could use either, but the advantage of RMSE is that it will come out in more interpretable units. For example, if you were building a model tha... | Relationship between RMSE and RSS
Having the mathematical derivations, you might ask yourself why use one measure over the other to assess the performance of a given model? You could use either, but the advantage of RMSE is that it wi |
44,058 | Relationship between RMSE and RSS | The RSS is the sum of the square of the errors (difference between calculation and measurement, or estimated and real values):
$ RSS = \sum{(\hat Y_i-Y_i)^2} $
The MSE is the mean of that sum of the square of the errors:
$ MSE = \frac{1}{n}\sum{(\hat Y_i-Y_i)^2}$
The RMSE is the square root of the MSE:
$ RMSE = \s... | Relationship between RMSE and RSS | The RSS is the sum of the square of the errors (difference between calculation and measurement, or estimated and real values):
$ RSS = \sum{(\hat Y_i-Y_i)^2} $
The MSE is the mean of that sum of the | Relationship between RMSE and RSS
The RSS is the sum of the square of the errors (difference between calculation and measurement, or estimated and real values):
$ RSS = \sum{(\hat Y_i-Y_i)^2} $
The MSE is the mean of that sum of the square of the errors:
$ MSE = \frac{1}{n}\sum{(\hat Y_i-Y_i)^2}$
The RMSE is the sq... | Relationship between RMSE and RSS
The RSS is the sum of the square of the errors (difference between calculation and measurement, or estimated and real values):
$ RSS = \sum{(\hat Y_i-Y_i)^2} $
The MSE is the mean of that sum of the |
44,059 | What does "irregularly spaced spatial data" mean? | A lot of techniques assume that data is sampled at regularly-spaced intervals. You might count how much litter is near each mile marker on the highway, or sample points in a forest on a regularly spaced grid (100, 200, 300, ... meters north and 100, 200, 300 meters east of some landmark). This also occurs in time--my E... | What does "irregularly spaced spatial data" mean? | A lot of techniques assume that data is sampled at regularly-spaced intervals. You might count how much litter is near each mile marker on the highway, or sample points in a forest on a regularly spac | What does "irregularly spaced spatial data" mean?
A lot of techniques assume that data is sampled at regularly-spaced intervals. You might count how much litter is near each mile marker on the highway, or sample points in a forest on a regularly spaced grid (100, 200, 300, ... meters north and 100, 200, 300 meters east... | What does "irregularly spaced spatial data" mean?
A lot of techniques assume that data is sampled at regularly-spaced intervals. You might count how much litter is near each mile marker on the highway, or sample points in a forest on a regularly spac |
44,060 | What does "irregularly spaced spatial data" mean? | Good answers by Matt (+1) and others. Just to have a picture to drive the message (visually) home. In the following figure assuming that the squares represent sampling points the grey boxes follow an obvious regularly spaced design; the red box are just random samples that are irregularly spaced.
Both designs have the... | What does "irregularly spaced spatial data" mean? | Good answers by Matt (+1) and others. Just to have a picture to drive the message (visually) home. In the following figure assuming that the squares represent sampling points the grey boxes follow an | What does "irregularly spaced spatial data" mean?
Good answers by Matt (+1) and others. Just to have a picture to drive the message (visually) home. In the following figure assuming that the squares represent sampling points the grey boxes follow an obvious regularly spaced design; the red box are just random samples t... | What does "irregularly spaced spatial data" mean?
Good answers by Matt (+1) and others. Just to have a picture to drive the message (visually) home. In the following figure assuming that the squares represent sampling points the grey boxes follow an |
44,061 | What does "irregularly spaced spatial data" mean? | This usually means that there is no clear underlying structure of the position of the points. I.e. it is not a rectangular grid or anything that can be represented compactly which has a clear structure.
Imagine that you have weather stations around a country and you are monitoring temperature. These weather stations ar... | What does "irregularly spaced spatial data" mean? | This usually means that there is no clear underlying structure of the position of the points. I.e. it is not a rectangular grid or anything that can be represented compactly which has a clear structur | What does "irregularly spaced spatial data" mean?
This usually means that there is no clear underlying structure of the position of the points. I.e. it is not a rectangular grid or anything that can be represented compactly which has a clear structure.
Imagine that you have weather stations around a country and you are... | What does "irregularly spaced spatial data" mean?
This usually means that there is no clear underlying structure of the position of the points. I.e. it is not a rectangular grid or anything that can be represented compactly which has a clear structur |
44,062 | What does "irregularly spaced spatial data" mean? | It's a british way of saying that your data does not come evenly spaced. Say, you measure the temperature on the road, and obtain the observation every 1 mile apart. This would be regularly spaced data. As opposed to taking measurements at every gas station, which would not be equally spaced, of course. | What does "irregularly spaced spatial data" mean? | It's a british way of saying that your data does not come evenly spaced. Say, you measure the temperature on the road, and obtain the observation every 1 mile apart. This would be regularly spaced dat | What does "irregularly spaced spatial data" mean?
It's a british way of saying that your data does not come evenly spaced. Say, you measure the temperature on the road, and obtain the observation every 1 mile apart. This would be regularly spaced data. As opposed to taking measurements at every gas station, which would... | What does "irregularly spaced spatial data" mean?
It's a british way of saying that your data does not come evenly spaced. Say, you measure the temperature on the road, and obtain the observation every 1 mile apart. This would be regularly spaced dat |
44,063 | How can I explain proportional odds models to a layman? | I think that the first and biggest hurdle is making sure that people indeed understand logistic regression and what an odds ratio actually is. If they get that far, you simply need to explain that proportional odds models take logistic regression one step further to account for ordered categorical responses.
A naive a... | How can I explain proportional odds models to a layman? | I think that the first and biggest hurdle is making sure that people indeed understand logistic regression and what an odds ratio actually is. If they get that far, you simply need to explain that pro | How can I explain proportional odds models to a layman?
I think that the first and biggest hurdle is making sure that people indeed understand logistic regression and what an odds ratio actually is. If they get that far, you simply need to explain that proportional odds models take logistic regression one step further ... | How can I explain proportional odds models to a layman?
I think that the first and biggest hurdle is making sure that people indeed understand logistic regression and what an odds ratio actually is. If they get that far, you simply need to explain that pro |
44,064 | How can I explain proportional odds models to a layman? | A key step is to make sure people understand why log-odds-ratios are useful. To help motivate log-odds-ratios, try the tale of two principals:
High School A reduced the dropout rate from 10% to 5%, a dramatic 50% decrease!
High School B increased the graduation rate from 90% to 95%, a modest 5.5% increase.
The firs... | How can I explain proportional odds models to a layman? | A key step is to make sure people understand why log-odds-ratios are useful. To help motivate log-odds-ratios, try the tale of two principals:
High School A reduced the dropout rate from 10% to 5%, | How can I explain proportional odds models to a layman?
A key step is to make sure people understand why log-odds-ratios are useful. To help motivate log-odds-ratios, try the tale of two principals:
High School A reduced the dropout rate from 10% to 5%, a dramatic 50% decrease!
High School B increased the graduation... | How can I explain proportional odds models to a layman?
A key step is to make sure people understand why log-odds-ratios are useful. To help motivate log-odds-ratios, try the tale of two principals:
High School A reduced the dropout rate from 10% to 5%, |
44,065 | Statistical significance of birth month of professional boxers | The results, as reported, are not statistically significant.
We can arrive at this conclusion (and better understand how it is meant to be interpreted) in steps. The first step is to take to heart Scortchi's comment,
Beware of data dredging.
This is the process of looking for "patterns" in data, finding one, and th... | Statistical significance of birth month of professional boxers | The results, as reported, are not statistically significant.
We can arrive at this conclusion (and better understand how it is meant to be interpreted) in steps. The first step is to take to heart Sc | Statistical significance of birth month of professional boxers
The results, as reported, are not statistically significant.
We can arrive at this conclusion (and better understand how it is meant to be interpreted) in steps. The first step is to take to heart Scortchi's comment,
Beware of data dredging.
This is the... | Statistical significance of birth month of professional boxers
The results, as reported, are not statistically significant.
We can arrive at this conclusion (and better understand how it is meant to be interpreted) in steps. The first step is to take to heart Sc |
44,066 | Statistical significance of birth month of professional boxers | A basic approach
You should be able to find data on births by time of year for the population as a whole.
To see if there is evidence that boxers have a different distribution of birth dates, given your sample size I suggest you work at a granularity of "months". Your null hypothesis is that boxers' birth months follow... | Statistical significance of birth month of professional boxers | A basic approach
You should be able to find data on births by time of year for the population as a whole.
To see if there is evidence that boxers have a different distribution of birth dates, given yo | Statistical significance of birth month of professional boxers
A basic approach
You should be able to find data on births by time of year for the population as a whole.
To see if there is evidence that boxers have a different distribution of birth dates, given your sample size I suggest you work at a granularity of "mo... | Statistical significance of birth month of professional boxers
A basic approach
You should be able to find data on births by time of year for the population as a whole.
To see if there is evidence that boxers have a different distribution of birth dates, given yo |
44,067 | What is the probability distribution of $1-\text{mean}(|A-B|)$ where $A$ and $B$ are independent U(0,1)? | $A-B$ has a symmetric triangular distribution on $(-1,1)$. It has mean 0 and variance $\frac{1}{6}$.
$|A-B|$ has a $\text{beta}(1,2)$ distribution. It has mean $\frac{1}{3}$ and variance $\frac{1}{18}$.
The distribution of a sum of beta random variables is known for $n=2$ (see 1).
Edit:
Actually, this particular beta i... | What is the probability distribution of $1-\text{mean}(|A-B|)$ where $A$ and $B$ are independent U(0 | $A-B$ has a symmetric triangular distribution on $(-1,1)$. It has mean 0 and variance $\frac{1}{6}$.
$|A-B|$ has a $\text{beta}(1,2)$ distribution. It has mean $\frac{1}{3}$ and variance $\frac{1}{18} | What is the probability distribution of $1-\text{mean}(|A-B|)$ where $A$ and $B$ are independent U(0,1)?
$A-B$ has a symmetric triangular distribution on $(-1,1)$. It has mean 0 and variance $\frac{1}{6}$.
$|A-B|$ has a $\text{beta}(1,2)$ distribution. It has mean $\frac{1}{3}$ and variance $\frac{1}{18}$.
The distribu... | What is the probability distribution of $1-\text{mean}(|A-B|)$ where $A$ and $B$ are independent U(0
$A-B$ has a symmetric triangular distribution on $(-1,1)$. It has mean 0 and variance $\frac{1}{6}$.
$|A-B|$ has a $\text{beta}(1,2)$ distribution. It has mean $\frac{1}{3}$ and variance $\frac{1}{18} |
44,068 | What is the probability distribution of $1-\text{mean}(|A-B|)$ where $A$ and $B$ are independent U(0,1)? | If $A$ and $B$ are standard Uniform and independent, then $(A-B) \sim Triangular(-1,0,1)$.
Then $Z = |A-B|$ will have pdf $f(z)$:
f = 2 (1 - z); domain[f] = {z, 0, 1};
Then, the characteristic function (cf) of the sample mean of $Z$ is $\big(E\big[e^{\large i \frac{t}{n} z}\big] \big)^n$:
(source: tri.org.au)
wh... | What is the probability distribution of $1-\text{mean}(|A-B|)$ where $A$ and $B$ are independent U(0 | If $A$ and $B$ are standard Uniform and independent, then $(A-B) \sim Triangular(-1,0,1)$.
Then $Z = |A-B|$ will have pdf $f(z)$:
f = 2 (1 - z); domain[f] = {z, 0, 1};
Then, the characteristic fu | What is the probability distribution of $1-\text{mean}(|A-B|)$ where $A$ and $B$ are independent U(0,1)?
If $A$ and $B$ are standard Uniform and independent, then $(A-B) \sim Triangular(-1,0,1)$.
Then $Z = |A-B|$ will have pdf $f(z)$:
f = 2 (1 - z); domain[f] = {z, 0, 1};
Then, the characteristic function (cf) of ... | What is the probability distribution of $1-\text{mean}(|A-B|)$ where $A$ and $B$ are independent U(0
If $A$ and $B$ are standard Uniform and independent, then $(A-B) \sim Triangular(-1,0,1)$.
Then $Z = |A-B|$ will have pdf $f(z)$:
f = 2 (1 - z); domain[f] = {z, 0, 1};
Then, the characteristic fu |
44,069 | How to refer to AIC model-averaged parameters and confidence intervals | If you have read Burnham & Anderson's monograph, you know just why they discourage AIC(c)-based model selection: because they subscribe to the theory of tapering effect sizes. In a nutshell, they posit that everything has an effect - it's just that most effects are pretty small (sort of a "long tail"). Thus, an AIC(c)-... | How to refer to AIC model-averaged parameters and confidence intervals | If you have read Burnham & Anderson's monograph, you know just why they discourage AIC(c)-based model selection: because they subscribe to the theory of tapering effect sizes. In a nutshell, they posi | How to refer to AIC model-averaged parameters and confidence intervals
If you have read Burnham & Anderson's monograph, you know just why they discourage AIC(c)-based model selection: because they subscribe to the theory of tapering effect sizes. In a nutshell, they posit that everything has an effect - it's just that ... | How to refer to AIC model-averaged parameters and confidence intervals
If you have read Burnham & Anderson's monograph, you know just why they discourage AIC(c)-based model selection: because they subscribe to the theory of tapering effect sizes. In a nutshell, they posi |
44,070 | How to refer to AIC model-averaged parameters and confidence intervals | If you have access, Ive found several papers that are very helpful when deciding what to report, what values to use and the common mistakes people make when using AIC. On mistake talked about is using 95% CI when you've used AIC procedures as discussed in Arnold 2010.
Arnold T.W. 2010. Uninformative Parameters and M... | How to refer to AIC model-averaged parameters and confidence intervals | If you have access, Ive found several papers that are very helpful when deciding what to report, what values to use and the common mistakes people make when using AIC. On mistake talked about is usin | How to refer to AIC model-averaged parameters and confidence intervals
If you have access, Ive found several papers that are very helpful when deciding what to report, what values to use and the common mistakes people make when using AIC. On mistake talked about is using 95% CI when you've used AIC procedures as discu... | How to refer to AIC model-averaged parameters and confidence intervals
If you have access, Ive found several papers that are very helpful when deciding what to report, what values to use and the common mistakes people make when using AIC. On mistake talked about is usin |
44,071 | How to refer to AIC model-averaged parameters and confidence intervals | Use the (AICcmodavg) package in R developed by Marc J. Mazerolle. This package will allow you to compute model average estimates and their 95% confidence intervals based on your entire list of candidate models. The estimates are weighted based on the relative importance of your models (the AIC values/ranking of your mo... | How to refer to AIC model-averaged parameters and confidence intervals | Use the (AICcmodavg) package in R developed by Marc J. Mazerolle. This package will allow you to compute model average estimates and their 95% confidence intervals based on your entire list of candida | How to refer to AIC model-averaged parameters and confidence intervals
Use the (AICcmodavg) package in R developed by Marc J. Mazerolle. This package will allow you to compute model average estimates and their 95% confidence intervals based on your entire list of candidate models. The estimates are weighted based on th... | How to refer to AIC model-averaged parameters and confidence intervals
Use the (AICcmodavg) package in R developed by Marc J. Mazerolle. This package will allow you to compute model average estimates and their 95% confidence intervals based on your entire list of candida |
44,072 | How to refer to AIC model-averaged parameters and confidence intervals | While @Stephan Kolassa's answer was probably best I'd like to just add that writing "the parameter was x.x and its CI does not cross zero" is not just laborious but treats the reader like an imbecile. When dealing with CIs simply use them as parameter estimates and if they don't cross zero that will be completely self ... | How to refer to AIC model-averaged parameters and confidence intervals | While @Stephan Kolassa's answer was probably best I'd like to just add that writing "the parameter was x.x and its CI does not cross zero" is not just laborious but treats the reader like an imbecile. | How to refer to AIC model-averaged parameters and confidence intervals
While @Stephan Kolassa's answer was probably best I'd like to just add that writing "the parameter was x.x and its CI does not cross zero" is not just laborious but treats the reader like an imbecile. When dealing with CIs simply use them as paramet... | How to refer to AIC model-averaged parameters and confidence intervals
While @Stephan Kolassa's answer was probably best I'd like to just add that writing "the parameter was x.x and its CI does not cross zero" is not just laborious but treats the reader like an imbecile. |
44,073 | What to call this graph showing icons for artists on a horizontal axis indicating number of unique words used? | It is the beeswarm version of a stripchart, with photos of the artists in place of dots. | What to call this graph showing icons for artists on a horizontal axis indicating number of unique w | It is the beeswarm version of a stripchart, with photos of the artists in place of dots. | What to call this graph showing icons for artists on a horizontal axis indicating number of unique words used?
It is the beeswarm version of a stripchart, with photos of the artists in place of dots. | What to call this graph showing icons for artists on a horizontal axis indicating number of unique w
It is the beeswarm version of a stripchart, with photos of the artists in place of dots. |
44,074 | What to call this graph showing icons for artists on a horizontal axis indicating number of unique words used? | There is a single numeric axis against which values are plotted and there is some mix of stacking and jittering to separate points that might occlude or overlap each other. Short of the photos, which make the graph distinctive, I have come across the following names for broadly similar plots:
barcode charts
beeswarm ... | What to call this graph showing icons for artists on a horizontal axis indicating number of unique w | There is a single numeric axis against which values are plotted and there is some mix of stacking and jittering to separate points that might occlude or overlap each other. Short of the photos, which | What to call this graph showing icons for artists on a horizontal axis indicating number of unique words used?
There is a single numeric axis against which values are plotted and there is some mix of stacking and jittering to separate points that might occlude or overlap each other. Short of the photos, which make the ... | What to call this graph showing icons for artists on a horizontal axis indicating number of unique w
There is a single numeric axis against which values are plotted and there is some mix of stacking and jittering to separate points that might occlude or overlap each other. Short of the photos, which |
44,075 | What to call this graph showing icons for artists on a horizontal axis indicating number of unique words used? | This is a dot chart with some non-random jittering for legibility.
It it not a dot plot, though there's some superficial similarity. | What to call this graph showing icons for artists on a horizontal axis indicating number of unique w | This is a dot chart with some non-random jittering for legibility.
It it not a dot plot, though there's some superficial similarity. | What to call this graph showing icons for artists on a horizontal axis indicating number of unique words used?
This is a dot chart with some non-random jittering for legibility.
It it not a dot plot, though there's some superficial similarity. | What to call this graph showing icons for artists on a horizontal axis indicating number of unique w
This is a dot chart with some non-random jittering for legibility.
It it not a dot plot, though there's some superficial similarity. |
44,076 | How can I improve the predictive power of this logistic regression model? | Summary
You appear to be looking at the associations between symptoms (a, b, c, d, and e, coded as linear, numeric variables) and cancer status (yes versus no, coded in binary).
Associations versus predictions
I think you are looking at associations between the symptoms and cancer status rather than the ability of the ... | How can I improve the predictive power of this logistic regression model? | Summary
You appear to be looking at the associations between symptoms (a, b, c, d, and e, coded as linear, numeric variables) and cancer status (yes versus no, coded in binary).
Associations versus pr | How can I improve the predictive power of this logistic regression model?
Summary
You appear to be looking at the associations between symptoms (a, b, c, d, and e, coded as linear, numeric variables) and cancer status (yes versus no, coded in binary).
Associations versus predictions
I think you are looking at associati... | How can I improve the predictive power of this logistic regression model?
Summary
You appear to be looking at the associations between symptoms (a, b, c, d, and e, coded as linear, numeric variables) and cancer status (yes versus no, coded in binary).
Associations versus pr |
44,077 | How can I improve the predictive power of this logistic regression model? | One thing to check is whether there is a linear relationship between the log odds of cancer and each of your 5 predictor variables. This is an assumption in logistic regression. If this does not hold you might want to consider adding higher order terms to the model, or even a nonlinear relationship between log odds of ... | How can I improve the predictive power of this logistic regression model? | One thing to check is whether there is a linear relationship between the log odds of cancer and each of your 5 predictor variables. This is an assumption in logistic regression. If this does not hold | How can I improve the predictive power of this logistic regression model?
One thing to check is whether there is a linear relationship between the log odds of cancer and each of your 5 predictor variables. This is an assumption in logistic regression. If this does not hold you might want to consider adding higher order... | How can I improve the predictive power of this logistic regression model?
One thing to check is whether there is a linear relationship between the log odds of cancer and each of your 5 predictor variables. This is an assumption in logistic regression. If this does not hold |
44,078 | How can I improve the predictive power of this logistic regression model? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
Ignore the classification tables completely. They are... | How can I improve the predictive power of this logistic regression model? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| How can I improve the predictive power of this logistic regression model?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | How can I improve the predictive power of this logistic regression model?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
44,079 | Do you use a chi-squared test or a t-test for equality of variances? | You do neither a T-test nor a $\chi^{2}$ test when testing $H_0: \sigma^{2}_X = \sigma^2_Y$ against $H_a: \sigma^{2}_X \neq \sigma^2_Y$. For testing the equality of variances between two normally distributed populations you use the F-test of equality of variances, which reformulates your test as $H_0: \frac{\sigma^{2}_... | Do you use a chi-squared test or a t-test for equality of variances? | You do neither a T-test nor a $\chi^{2}$ test when testing $H_0: \sigma^{2}_X = \sigma^2_Y$ against $H_a: \sigma^{2}_X \neq \sigma^2_Y$. For testing the equality of variances between two normally dist | Do you use a chi-squared test or a t-test for equality of variances?
You do neither a T-test nor a $\chi^{2}$ test when testing $H_0: \sigma^{2}_X = \sigma^2_Y$ against $H_a: \sigma^{2}_X \neq \sigma^2_Y$. For testing the equality of variances between two normally distributed populations you use the F-test of equality ... | Do you use a chi-squared test or a t-test for equality of variances?
You do neither a T-test nor a $\chi^{2}$ test when testing $H_0: \sigma^{2}_X = \sigma^2_Y$ against $H_a: \sigma^{2}_X \neq \sigma^2_Y$. For testing the equality of variances between two normally dist |
44,080 | Do you use a chi-squared test or a t-test for equality of variances? | The test you get with chisq.test is for counts - used to compare proportions or test for independence with categorical data, that kind of thing.
On the other hand, t-tests are usually for comparing means.
There is a test involving variances (a one sample variance test) with normal data that is a chisquare test but you ... | Do you use a chi-squared test or a t-test for equality of variances? | The test you get with chisq.test is for counts - used to compare proportions or test for independence with categorical data, that kind of thing.
On the other hand, t-tests are usually for comparing me | Do you use a chi-squared test or a t-test for equality of variances?
The test you get with chisq.test is for counts - used to compare proportions or test for independence with categorical data, that kind of thing.
On the other hand, t-tests are usually for comparing means.
There is a test involving variances (a one sam... | Do you use a chi-squared test or a t-test for equality of variances?
The test you get with chisq.test is for counts - used to compare proportions or test for independence with categorical data, that kind of thing.
On the other hand, t-tests are usually for comparing me |
44,081 | Do you use a chi-squared test or a t-test for equality of variances? | @Mona Jalal, There are various tests used for equality of variances, suited for different situations each having its advantages and limitations. The most common ones are
Bartlett's Test of Sphericity
Levene's test
F- Test
While the post is continuously going back and forth here, may be you want to discuss them i... | Do you use a chi-squared test or a t-test for equality of variances? | @Mona Jalal, There are various tests used for equality of variances, suited for different situations each having its advantages and limitations. The most common ones are
Bartlett's Test of Sphericit | Do you use a chi-squared test or a t-test for equality of variances?
@Mona Jalal, There are various tests used for equality of variances, suited for different situations each having its advantages and limitations. The most common ones are
Bartlett's Test of Sphericity
Levene's test
F- Test
While the post is cont... | Do you use a chi-squared test or a t-test for equality of variances?
@Mona Jalal, There are various tests used for equality of variances, suited for different situations each having its advantages and limitations. The most common ones are
Bartlett's Test of Sphericit |
44,082 | Do you use a chi-squared test or a t-test for equality of variances? | Note that t.test is for a difference of means, when you actually want to test for a difference of variances based on the null and alternative hypotheses you set up. See:
?var.test
var.test(x, y) | Do you use a chi-squared test or a t-test for equality of variances? | Note that t.test is for a difference of means, when you actually want to test for a difference of variances based on the null and alternative hypotheses you set up. See:
?var.test
var.test(x, y) | Do you use a chi-squared test or a t-test for equality of variances?
Note that t.test is for a difference of means, when you actually want to test for a difference of variances based on the null and alternative hypotheses you set up. See:
?var.test
var.test(x, y) | Do you use a chi-squared test or a t-test for equality of variances?
Note that t.test is for a difference of means, when you actually want to test for a difference of variances based on the null and alternative hypotheses you set up. See:
?var.test
var.test(x, y) |
44,083 | Spline fitting in R - how to force passing two data points? | Rather than use smooth.spline() in the stats package, there is a function cobs() in the cobs package that allows you to do exactly the sort of thing you want. COBS stands for Constrained B-splines. Possible constraints include going through specific points, setting derivatives to specified values, monotonicity (increas... | Spline fitting in R - how to force passing two data points? | Rather than use smooth.spline() in the stats package, there is a function cobs() in the cobs package that allows you to do exactly the sort of thing you want. COBS stands for Constrained B-splines. Po | Spline fitting in R - how to force passing two data points?
Rather than use smooth.spline() in the stats package, there is a function cobs() in the cobs package that allows you to do exactly the sort of thing you want. COBS stands for Constrained B-splines. Possible constraints include going through specific points, se... | Spline fitting in R - how to force passing two data points?
Rather than use smooth.spline() in the stats package, there is a function cobs() in the cobs package that allows you to do exactly the sort of thing you want. COBS stands for Constrained B-splines. Po |
44,084 | Spline fitting in R - how to force passing two data points? | I cannot think of any way to do it using smooth.spline. If you were to use a spline basis such as bs from the splines package, then you could possibly do this using quadradic programming to constrain the endpoints, but it could be complicated figuring out the constraints.
Here is an approach that uses xsplines (diff... | Spline fitting in R - how to force passing two data points? | I cannot think of any way to do it using smooth.spline. If you were to use a spline basis such as bs from the splines package, then you could possibly do this using quadradic programming to constrain | Spline fitting in R - how to force passing two data points?
I cannot think of any way to do it using smooth.spline. If you were to use a spline basis such as bs from the splines package, then you could possibly do this using quadradic programming to constrain the endpoints, but it could be complicated figuring out the... | Spline fitting in R - how to force passing two data points?
I cannot think of any way to do it using smooth.spline. If you were to use a spline basis such as bs from the splines package, then you could possibly do this using quadradic programming to constrain |
44,085 | What is the advantage of having balanced panel data rather than unbalanced? | I believe these are largely historical reasons. In the 1940s, one had to conduct analysis of variance with paper and pencil, so having balanced designs led to simple sums for both means and variances. Any imbalance would require inverting matrices 4x4 or larger (I've done it a couple of times on regression exams, and n... | What is the advantage of having balanced panel data rather than unbalanced? | I believe these are largely historical reasons. In the 1940s, one had to conduct analysis of variance with paper and pencil, so having balanced designs led to simple sums for both means and variances. | What is the advantage of having balanced panel data rather than unbalanced?
I believe these are largely historical reasons. In the 1940s, one had to conduct analysis of variance with paper and pencil, so having balanced designs led to simple sums for both means and variances. Any imbalance would require inverting matri... | What is the advantage of having balanced panel data rather than unbalanced?
I believe these are largely historical reasons. In the 1940s, one had to conduct analysis of variance with paper and pencil, so having balanced designs led to simple sums for both means and variances. |
44,086 | What is the advantage of having balanced panel data rather than unbalanced? | I think whenever you have unbalanced panels, you need to come up with a formal description of why that is the case. You need to worry about self-selection, nonresponse, and attrition, especially if you're interested in population parameters and consistency. For most estimators, the mechanics are largely the same. | What is the advantage of having balanced panel data rather than unbalanced? | I think whenever you have unbalanced panels, you need to come up with a formal description of why that is the case. You need to worry about self-selection, nonresponse, and attrition, especially if yo | What is the advantage of having balanced panel data rather than unbalanced?
I think whenever you have unbalanced panels, you need to come up with a formal description of why that is the case. You need to worry about self-selection, nonresponse, and attrition, especially if you're interested in population parameters and... | What is the advantage of having balanced panel data rather than unbalanced?
I think whenever you have unbalanced panels, you need to come up with a formal description of why that is the case. You need to worry about self-selection, nonresponse, and attrition, especially if yo |
44,087 | What is the advantage of having balanced panel data rather than unbalanced? | Balanced data is preferred over unbalanced panels, because it allows an observation of the same unit (e.g., individual, company, person, etc.) in every time period (e.g., year, month, etc.), which reduces the noise introduced by unit (individual, etc.) heterogeneity. | What is the advantage of having balanced panel data rather than unbalanced? | Balanced data is preferred over unbalanced panels, because it allows an observation of the same unit (e.g., individual, company, person, etc.) in every time period (e.g., year, month, etc.), which red | What is the advantage of having balanced panel data rather than unbalanced?
Balanced data is preferred over unbalanced panels, because it allows an observation of the same unit (e.g., individual, company, person, etc.) in every time period (e.g., year, month, etc.), which reduces the noise introduced by unit (individua... | What is the advantage of having balanced panel data rather than unbalanced?
Balanced data is preferred over unbalanced panels, because it allows an observation of the same unit (e.g., individual, company, person, etc.) in every time period (e.g., year, month, etc.), which red |
44,088 | How can 8 dimensions be reduced to 3? | Ion, PCA is just a specific case of orthogonal rotation. Let X be your n x p data matrix of n points in p dimensions (axes). To obtain this same cloud of points in a new set of axes somehow rotated in space relatively the old ones, you multiply X by a p x p matrix Q of cosines between the old axes (rows) and new axes (... | How can 8 dimensions be reduced to 3? | Ion, PCA is just a specific case of orthogonal rotation. Let X be your n x p data matrix of n points in p dimensions (axes). To obtain this same cloud of points in a new set of axes somehow rotated in | How can 8 dimensions be reduced to 3?
Ion, PCA is just a specific case of orthogonal rotation. Let X be your n x p data matrix of n points in p dimensions (axes). To obtain this same cloud of points in a new set of axes somehow rotated in space relatively the old ones, you multiply X by a p x p matrix Q of cosines betw... | How can 8 dimensions be reduced to 3?
Ion, PCA is just a specific case of orthogonal rotation. Let X be your n x p data matrix of n points in p dimensions (axes). To obtain this same cloud of points in a new set of axes somehow rotated in |
44,089 | How can 8 dimensions be reduced to 3? | +1 for ttnphns, but I'll try to give tl;dr, math free version.
Your doubts are fully justified -- one cannot stuff 8 dims as <8 linear combinations in a general case. What PCA really does is that it converts 8 dims into 8 linear combinations in such a way that it stuffs how much diversity of the data possible to the f... | How can 8 dimensions be reduced to 3? | +1 for ttnphns, but I'll try to give tl;dr, math free version.
Your doubts are fully justified -- one cannot stuff 8 dims as <8 linear combinations in a general case. What PCA really does is that it | How can 8 dimensions be reduced to 3?
+1 for ttnphns, but I'll try to give tl;dr, math free version.
Your doubts are fully justified -- one cannot stuff 8 dims as <8 linear combinations in a general case. What PCA really does is that it converts 8 dims into 8 linear combinations in such a way that it stuffs how much d... | How can 8 dimensions be reduced to 3?
+1 for ttnphns, but I'll try to give tl;dr, math free version.
Your doubts are fully justified -- one cannot stuff 8 dims as <8 linear combinations in a general case. What PCA really does is that it |
44,090 | How can 8 dimensions be reduced to 3? | Here's my stab at this; completely without math, just some basic principles and a picture. You asked for it. ;)
Consider the scenario in the picture below. You have 2D data points along the X and Y axis. You could use the PCA to find the principal axis P.
The point of this analysis is that if your data are distribute... | How can 8 dimensions be reduced to 3? | Here's my stab at this; completely without math, just some basic principles and a picture. You asked for it. ;)
Consider the scenario in the picture below. You have 2D data points along the X and Y a | How can 8 dimensions be reduced to 3?
Here's my stab at this; completely without math, just some basic principles and a picture. You asked for it. ;)
Consider the scenario in the picture below. You have 2D data points along the X and Y axis. You could use the PCA to find the principal axis P.
The point of this analys... | How can 8 dimensions be reduced to 3?
Here's my stab at this; completely without math, just some basic principles and a picture. You asked for it. ;)
Consider the scenario in the picture below. You have 2D data points along the X and Y a |
44,091 | How to convert a vector of enumerable strings into a vector of numbers? [closed] | Here is a possibility, very similar than that of @Roman Lustrik, but just a little bit more automatic.
Say that
x <- c("a", "b", "b", "c")
Then
> x <- as.factor(x)
> levels(x) <- 1:length(levels(x))
> x <- as.numeric(x)
makes the job:
> print(x)
[1] 1 2 2 3 | How to convert a vector of enumerable strings into a vector of numbers? [closed] | Here is a possibility, very similar than that of @Roman Lustrik, but just a little bit more automatic.
Say that
x <- c("a", "b", "b", "c")
Then
> x <- as.factor(x)
> levels(x) <- 1:length(leve | How to convert a vector of enumerable strings into a vector of numbers? [closed]
Here is a possibility, very similar than that of @Roman Lustrik, but just a little bit more automatic.
Say that
x <- c("a", "b", "b", "c")
Then
> x <- as.factor(x)
> levels(x) <- 1:length(levels(x))
> x <- as.numeric(x)
makes t... | How to convert a vector of enumerable strings into a vector of numbers? [closed]
Here is a possibility, very similar than that of @Roman Lustrik, but just a little bit more automatic.
Say that
x <- c("a", "b", "b", "c")
Then
> x <- as.factor(x)
> levels(x) <- 1:length(leve |
44,092 | How to convert a vector of enumerable strings into a vector of numbers? [closed] | Another programming question has sneaked...
Anyway, the faster way is
unclass(factor(x))
additionally one can add levels(...)<-NULL to remove the redundant attribute too (not much required inside a script). | How to convert a vector of enumerable strings into a vector of numbers? [closed] | Another programming question has sneaked...
Anyway, the faster way is
unclass(factor(x))
additionally one can add levels(...)<-NULL to remove the redundant attribute too (not much required inside a s | How to convert a vector of enumerable strings into a vector of numbers? [closed]
Another programming question has sneaked...
Anyway, the faster way is
unclass(factor(x))
additionally one can add levels(...)<-NULL to remove the redundant attribute too (not much required inside a script). | How to convert a vector of enumerable strings into a vector of numbers? [closed]
Another programming question has sneaked...
Anyway, the faster way is
unclass(factor(x))
additionally one can add levels(...)<-NULL to remove the redundant attribute too (not much required inside a s |
44,093 | How to convert a vector of enumerable strings into a vector of numbers? [closed] | There's a few ways of doing this. Here's one.
> (a <- as.factor(sample(letters[1:5], 30, replace = TRUE)))
[1] d a e e e c b e b b c a d d d d c b c c b b e b e b c d c b
Levels: a b c d e
> (levels(a) <- 1:5)
[1] 1 2 3 4 5
> a <- as.numeric(a) # convert these factors into numbers | How to convert a vector of enumerable strings into a vector of numbers? [closed] | There's a few ways of doing this. Here's one.
> (a <- as.factor(sample(letters[1:5], 30, replace = TRUE)))
[1] d a e e e c b e b b c a d d d d c b c c b b e b e b c d c b
Levels: a b c d e
> ( | How to convert a vector of enumerable strings into a vector of numbers? [closed]
There's a few ways of doing this. Here's one.
> (a <- as.factor(sample(letters[1:5], 30, replace = TRUE)))
[1] d a e e e c b e b b c a d d d d c b c c b b e b e b c d c b
Levels: a b c d e
> (levels(a) <- 1:5)
[1] 1 2 3 4 5
> a... | How to convert a vector of enumerable strings into a vector of numbers? [closed]
There's a few ways of doing this. Here's one.
> (a <- as.factor(sample(letters[1:5], 30, replace = TRUE)))
[1] d a e e e c b e b b c a d d d d c b c c b b e b e b c d c b
Levels: a b c d e
> ( |
44,094 | How to convert a vector of enumerable strings into a vector of numbers? [closed] | as.numeric(factor(c("d", "a", "b", "b", "c")))
[1] 4 1 2 2 3 | How to convert a vector of enumerable strings into a vector of numbers? [closed] | as.numeric(factor(c("d", "a", "b", "b", "c")))
[1] 4 1 2 2 3 | How to convert a vector of enumerable strings into a vector of numbers? [closed]
as.numeric(factor(c("d", "a", "b", "b", "c")))
[1] 4 1 2 2 3 | How to convert a vector of enumerable strings into a vector of numbers? [closed]
as.numeric(factor(c("d", "a", "b", "b", "c")))
[1] 4 1 2 2 3 |
44,095 | Comparing reproducible research strategies: brew or Sweave vs. R2HTML | New answer based on comment below:
As I understand, Method 1 is to mix R code and HTML or LaTeX in the same document, using Sweave or brew for example, to create a final document, while Method 2 is to use R code to generate HTML or LaTeX, using the R2HTML or Hmisc packages for example, and then to just run the R code t... | Comparing reproducible research strategies: brew or Sweave vs. R2HTML | New answer based on comment below:
As I understand, Method 1 is to mix R code and HTML or LaTeX in the same document, using Sweave or brew for example, to create a final document, while Method 2 is to | Comparing reproducible research strategies: brew or Sweave vs. R2HTML
New answer based on comment below:
As I understand, Method 1 is to mix R code and HTML or LaTeX in the same document, using Sweave or brew for example, to create a final document, while Method 2 is to use R code to generate HTML or LaTeX, using the R... | Comparing reproducible research strategies: brew or Sweave vs. R2HTML
New answer based on comment below:
As I understand, Method 1 is to mix R code and HTML or LaTeX in the same document, using Sweave or brew for example, to create a final document, while Method 2 is to |
44,096 | Comparing reproducible research strategies: brew or Sweave vs. R2HTML | These are just a few points.
If you want to just write simple reports, then the set of LaTeX commands that you need to learn is a lot smaller than if you want to do complex things.
An appealing aspect of LaTeX over some simple markup systems is that if you want features like referencing, automatic numbering, multi-pag... | Comparing reproducible research strategies: brew or Sweave vs. R2HTML | These are just a few points.
If you want to just write simple reports, then the set of LaTeX commands that you need to learn is a lot smaller than if you want to do complex things.
An appealing aspec | Comparing reproducible research strategies: brew or Sweave vs. R2HTML
These are just a few points.
If you want to just write simple reports, then the set of LaTeX commands that you need to learn is a lot smaller than if you want to do complex things.
An appealing aspect of LaTeX over some simple markup systems is that... | Comparing reproducible research strategies: brew or Sweave vs. R2HTML
These are just a few points.
If you want to just write simple reports, then the set of LaTeX commands that you need to learn is a lot smaller than if you want to do complex things.
An appealing aspec |
44,097 | Comparing reproducible research strategies: brew or Sweave vs. R2HTML | The other nicE thing potentially about LaTeX or another markup in the Sweave/odfWeave/asciiWeave paradigm is that for repeated reports you can template it a bit better once and then just reuse the template. See Harrell's rreport package as an example | Comparing reproducible research strategies: brew or Sweave vs. R2HTML | The other nicE thing potentially about LaTeX or another markup in the Sweave/odfWeave/asciiWeave paradigm is that for repeated reports you can template it a bit better once and then just reuse the tem | Comparing reproducible research strategies: brew or Sweave vs. R2HTML
The other nicE thing potentially about LaTeX or another markup in the Sweave/odfWeave/asciiWeave paradigm is that for repeated reports you can template it a bit better once and then just reuse the template. See Harrell's rreport package as an example | Comparing reproducible research strategies: brew or Sweave vs. R2HTML
The other nicE thing potentially about LaTeX or another markup in the Sweave/odfWeave/asciiWeave paradigm is that for repeated reports you can template it a bit better once and then just reuse the tem |
44,098 | Comparing reproducible research strategies: brew or Sweave vs. R2HTML | You're pretty safe in using either - though I confess I don't use either at all. I suspect the primary reason for the popularity of the LaTeX/Sweave method is the number of fields that use LaTeX as their primary paper/presentation/manuscript format that incentivizes using a LaTeX based system. I don't know of a single ... | Comparing reproducible research strategies: brew or Sweave vs. R2HTML | You're pretty safe in using either - though I confess I don't use either at all. I suspect the primary reason for the popularity of the LaTeX/Sweave method is the number of fields that use LaTeX as th | Comparing reproducible research strategies: brew or Sweave vs. R2HTML
You're pretty safe in using either - though I confess I don't use either at all. I suspect the primary reason for the popularity of the LaTeX/Sweave method is the number of fields that use LaTeX as their primary paper/presentation/manuscript format t... | Comparing reproducible research strategies: brew or Sweave vs. R2HTML
You're pretty safe in using either - though I confess I don't use either at all. I suspect the primary reason for the popularity of the LaTeX/Sweave method is the number of fields that use LaTeX as th |
44,099 | Comparing reproducible research strategies: brew or Sweave vs. R2HTML | The reason option 1 is so common is because...it is so common. Sweave has been around for the better part of 10 years and, for many R users, is synonymous with reproducible research. Furthermore, the sorts of people who would hear the phrase 'reproducible research' and think 'that sounds great' are probably likely to a... | Comparing reproducible research strategies: brew or Sweave vs. R2HTML | The reason option 1 is so common is because...it is so common. Sweave has been around for the better part of 10 years and, for many R users, is synonymous with reproducible research. Furthermore, the | Comparing reproducible research strategies: brew or Sweave vs. R2HTML
The reason option 1 is so common is because...it is so common. Sweave has been around for the better part of 10 years and, for many R users, is synonymous with reproducible research. Furthermore, the sorts of people who would hear the phrase 'reprodu... | Comparing reproducible research strategies: brew or Sweave vs. R2HTML
The reason option 1 is so common is because...it is so common. Sweave has been around for the better part of 10 years and, for many R users, is synonymous with reproducible research. Furthermore, the |
44,100 | What methods to use for statistical prediction/forecast of trading data? | Convert your series to day-to-day returns, and use the package PerformanceAnalytics in R.
I saved your attached data as a .csv file on my desktop. Here's some R code demonstrating how you could evaluate this trading strategy. Keep in mind that if you have any "look-ahead" or "data-snooping" bias in your trading model,... | What methods to use for statistical prediction/forecast of trading data? | Convert your series to day-to-day returns, and use the package PerformanceAnalytics in R.
I saved your attached data as a .csv file on my desktop. Here's some R code demonstrating how you could evalua | What methods to use for statistical prediction/forecast of trading data?
Convert your series to day-to-day returns, and use the package PerformanceAnalytics in R.
I saved your attached data as a .csv file on my desktop. Here's some R code demonstrating how you could evaluate this trading strategy. Keep in mind that if... | What methods to use for statistical prediction/forecast of trading data?
Convert your series to day-to-day returns, and use the package PerformanceAnalytics in R.
I saved your attached data as a .csv file on my desktop. Here's some R code demonstrating how you could evalua |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.