idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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1,701 | US Election results 2016: What went wrong with prediction models? | First it was Brexit, now the US election
Not really a first, e.g. the French presidential election, 2002 "led to serious discussions about polling techniques".
So it's not far-fetched to say these models didn't do a very good job.
Garbage in, garbage out.
I saw one explanation was voters were unwilling to identify ... | US Election results 2016: What went wrong with prediction models? | First it was Brexit, now the US election
Not really a first, e.g. the French presidential election, 2002 "led to serious discussions about polling techniques".
So it's not far-fetched to say these m | US Election results 2016: What went wrong with prediction models?
First it was Brexit, now the US election
Not really a first, e.g. the French presidential election, 2002 "led to serious discussions about polling techniques".
So it's not far-fetched to say these models didn't do a very good job.
Garbage in, garbage ... | US Election results 2016: What went wrong with prediction models?
First it was Brexit, now the US election
Not really a first, e.g. the French presidential election, 2002 "led to serious discussions about polling techniques".
So it's not far-fetched to say these m |
1,702 | US Election results 2016: What went wrong with prediction models? | The USC/LA Times poll has some accurate numbers. They predicted Trump to be in the lead. See The USC/L.A. Times poll saw what other surveys missed: A wave of Trump support
http://www.latimes.com/politics/la-na-pol-usc-latimes-poll-20161108-story.html
They had accurate numbers for 2012 as well.
You may want to review: ... | US Election results 2016: What went wrong with prediction models? | The USC/LA Times poll has some accurate numbers. They predicted Trump to be in the lead. See The USC/L.A. Times poll saw what other surveys missed: A wave of Trump support
http://www.latimes.com/polit | US Election results 2016: What went wrong with prediction models?
The USC/LA Times poll has some accurate numbers. They predicted Trump to be in the lead. See The USC/L.A. Times poll saw what other surveys missed: A wave of Trump support
http://www.latimes.com/politics/la-na-pol-usc-latimes-poll-20161108-story.html
Th... | US Election results 2016: What went wrong with prediction models?
The USC/LA Times poll has some accurate numbers. They predicted Trump to be in the lead. See The USC/L.A. Times poll saw what other surveys missed: A wave of Trump support
http://www.latimes.com/polit |
1,703 | US Election results 2016: What went wrong with prediction models? | No high ground claimed here. I work in a field (Monitoring and Evaluation) that is as rife with pseudo-science as any other social science you could name.
But here's the deal, the polling industry is supposedly in 'crisis' today because it got the US election predictions so wrong, social science in general has a replic... | US Election results 2016: What went wrong with prediction models? | No high ground claimed here. I work in a field (Monitoring and Evaluation) that is as rife with pseudo-science as any other social science you could name.
But here's the deal, the polling industry is | US Election results 2016: What went wrong with prediction models?
No high ground claimed here. I work in a field (Monitoring and Evaluation) that is as rife with pseudo-science as any other social science you could name.
But here's the deal, the polling industry is supposedly in 'crisis' today because it got the US ele... | US Election results 2016: What went wrong with prediction models?
No high ground claimed here. I work in a field (Monitoring and Evaluation) that is as rife with pseudo-science as any other social science you could name.
But here's the deal, the polling industry is |
1,704 | US Election results 2016: What went wrong with prediction models? | Polls tend to have an error margin of 5% that you can't really get rid of, because it's not a random error, but a bias. Even if you average across many polls, it does not get much better. This has to do with misrepresented voter groups, lack of mobilization, inability to go to the vote on a workday, unwillingness to an... | US Election results 2016: What went wrong with prediction models? | Polls tend to have an error margin of 5% that you can't really get rid of, because it's not a random error, but a bias. Even if you average across many polls, it does not get much better. This has to | US Election results 2016: What went wrong with prediction models?
Polls tend to have an error margin of 5% that you can't really get rid of, because it's not a random error, but a bias. Even if you average across many polls, it does not get much better. This has to do with misrepresented voter groups, lack of mobilizat... | US Election results 2016: What went wrong with prediction models?
Polls tend to have an error margin of 5% that you can't really get rid of, because it's not a random error, but a bias. Even if you average across many polls, it does not get much better. This has to |
1,705 | US Election results 2016: What went wrong with prediction models? | The reliance on data analysis had a huge impact in strategic campaign decisions, journalistic coverage, and ultimately in individual choices. What could possibly go wrong when the Clinton campaign's decisions were informed by no other than $\small 400,000$ daily simulations on the secret Ada algorithm?
In the end, it e... | US Election results 2016: What went wrong with prediction models? | The reliance on data analysis had a huge impact in strategic campaign decisions, journalistic coverage, and ultimately in individual choices. What could possibly go wrong when the Clinton campaign's d | US Election results 2016: What went wrong with prediction models?
The reliance on data analysis had a huge impact in strategic campaign decisions, journalistic coverage, and ultimately in individual choices. What could possibly go wrong when the Clinton campaign's decisions were informed by no other than $\small 400,00... | US Election results 2016: What went wrong with prediction models?
The reliance on data analysis had a huge impact in strategic campaign decisions, journalistic coverage, and ultimately in individual choices. What could possibly go wrong when the Clinton campaign's d |
1,706 | US Election results 2016: What went wrong with prediction models? | One of the reasons for poll inaccurracy in the US election, besides some people for whatever reason don´t say the truth is, that the "winner takes it all" effect makes predictions even less easier.
A 1% difference in one state can lead to a complete shift of a state and influence the whole outcome very heavily. Hillary... | US Election results 2016: What went wrong with prediction models? | One of the reasons for poll inaccurracy in the US election, besides some people for whatever reason don´t say the truth is, that the "winner takes it all" effect makes predictions even less easier.
A | US Election results 2016: What went wrong with prediction models?
One of the reasons for poll inaccurracy in the US election, besides some people for whatever reason don´t say the truth is, that the "winner takes it all" effect makes predictions even less easier.
A 1% difference in one state can lead to a complete shif... | US Election results 2016: What went wrong with prediction models?
One of the reasons for poll inaccurracy in the US election, besides some people for whatever reason don´t say the truth is, that the "winner takes it all" effect makes predictions even less easier.
A |
1,707 | US Election results 2016: What went wrong with prediction models? | (Just answering this bit, as the other answers seem to have covered everything else.)
As late as 4 pm PST yesterday, the betting markets were still favoring Hillary 4 to 1.
I take it that the betting markets, with real money on the line, should act as an ensemble of all the available prediction models out there.
No... | US Election results 2016: What went wrong with prediction models? | (Just answering this bit, as the other answers seem to have covered everything else.)
As late as 4 pm PST yesterday, the betting markets were still favoring Hillary 4 to 1.
I take it that the betti | US Election results 2016: What went wrong with prediction models?
(Just answering this bit, as the other answers seem to have covered everything else.)
As late as 4 pm PST yesterday, the betting markets were still favoring Hillary 4 to 1.
I take it that the betting markets, with real money on the line, should act as... | US Election results 2016: What went wrong with prediction models?
(Just answering this bit, as the other answers seem to have covered everything else.)
As late as 4 pm PST yesterday, the betting markets were still favoring Hillary 4 to 1.
I take it that the betti |
1,708 | US Election results 2016: What went wrong with prediction models? | It is not surprising that these efforts failed, when you consider the disparity between what information the models have access to and what information drives behavior at the polling booth. I'm speculating, but the models probably take into account:
a variety of pre-election polling results
historical state leanings (... | US Election results 2016: What went wrong with prediction models? | It is not surprising that these efforts failed, when you consider the disparity between what information the models have access to and what information drives behavior at the polling booth. I'm specul | US Election results 2016: What went wrong with prediction models?
It is not surprising that these efforts failed, when you consider the disparity between what information the models have access to and what information drives behavior at the polling booth. I'm speculating, but the models probably take into account:
a v... | US Election results 2016: What went wrong with prediction models?
It is not surprising that these efforts failed, when you consider the disparity between what information the models have access to and what information drives behavior at the polling booth. I'm specul |
1,709 | US Election results 2016: What went wrong with prediction models? | The polling models didn't consider how many Libertarians might switch from Johnson to Trump when it came to actual voting. The states which were won by a thin margin were won based on which percentage of the vote Johnson got. PA (which pushed Trump past 270 on the election night) gave only 2% to Johnson. NH (which w... | US Election results 2016: What went wrong with prediction models? | The polling models didn't consider how many Libertarians might switch from Johnson to Trump when it came to actual voting. The states which were won by a thin margin were won based on which percentag | US Election results 2016: What went wrong with prediction models?
The polling models didn't consider how many Libertarians might switch from Johnson to Trump when it came to actual voting. The states which were won by a thin margin were won based on which percentage of the vote Johnson got. PA (which pushed Trump pas... | US Election results 2016: What went wrong with prediction models?
The polling models didn't consider how many Libertarians might switch from Johnson to Trump when it came to actual voting. The states which were won by a thin margin were won based on which percentag |
1,710 | US Election results 2016: What went wrong with prediction models? | Polls are not historical trends. A Bayesian would inquire as to the historical trends. Since Abraham Lincoln, there has been a Republican party and a Democratic party holding the presidential office. The trend for party change 16 times since then from Wikipedia has the following cumulative mass function
where time in ... | US Election results 2016: What went wrong with prediction models? | Polls are not historical trends. A Bayesian would inquire as to the historical trends. Since Abraham Lincoln, there has been a Republican party and a Democratic party holding the presidential office. | US Election results 2016: What went wrong with prediction models?
Polls are not historical trends. A Bayesian would inquire as to the historical trends. Since Abraham Lincoln, there has been a Republican party and a Democratic party holding the presidential office. The trend for party change 16 times since then from Wi... | US Election results 2016: What went wrong with prediction models?
Polls are not historical trends. A Bayesian would inquire as to the historical trends. Since Abraham Lincoln, there has been a Republican party and a Democratic party holding the presidential office. |
1,711 | US Election results 2016: What went wrong with prediction models? | I think poll results were extrapolated to the extent of the public assuming the voter demographics will be similar to poll taker demographics and would be a good representation of the whole population. For example, if 7 out of 10 minorities supported Hillary in the polls, and if that minority represents 30% of the US p... | US Election results 2016: What went wrong with prediction models? | I think poll results were extrapolated to the extent of the public assuming the voter demographics will be similar to poll taker demographics and would be a good representation of the whole population | US Election results 2016: What went wrong with prediction models?
I think poll results were extrapolated to the extent of the public assuming the voter demographics will be similar to poll taker demographics and would be a good representation of the whole population. For example, if 7 out of 10 minorities supported Hil... | US Election results 2016: What went wrong with prediction models?
I think poll results were extrapolated to the extent of the public assuming the voter demographics will be similar to poll taker demographics and would be a good representation of the whole population |
1,712 | US Election results 2016: What went wrong with prediction models? | HoraceT and CliffAB (sorry too long for comments) I’m afraid I have a lifetime of examples, which have also taught me that I need to be very careful with their explanation, if I wish to avoid offending people. So while I don’t want your indulgence, I do ask for your patience. Here goes:
To start with an extreme examp... | US Election results 2016: What went wrong with prediction models? | HoraceT and CliffAB (sorry too long for comments) I’m afraid I have a lifetime of examples, which have also taught me that I need to be very careful with their explanation, if I wish to avoid offendin | US Election results 2016: What went wrong with prediction models?
HoraceT and CliffAB (sorry too long for comments) I’m afraid I have a lifetime of examples, which have also taught me that I need to be very careful with their explanation, if I wish to avoid offending people. So while I don’t want your indulgence, I do ... | US Election results 2016: What went wrong with prediction models?
HoraceT and CliffAB (sorry too long for comments) I’m afraid I have a lifetime of examples, which have also taught me that I need to be very careful with their explanation, if I wish to avoid offendin |
1,713 | How does the correlation coefficient differ from regression slope? | Assuming you're talking about a simple regression model $$Y_i = \alpha + \beta X_i + \varepsilon_i$$ estimated by least squares, we know from wikipedia that $$ \hat {\beta} = {\rm cor}(Y_i, X_i) \cdot \frac{ {\rm SD}(Y_i) }{ {\rm SD}(X_i) } $$ Therefore the two only coincide when ${\rm SD}(Y_i) = {\rm SD}(X_i)$. That i... | How does the correlation coefficient differ from regression slope? | Assuming you're talking about a simple regression model $$Y_i = \alpha + \beta X_i + \varepsilon_i$$ estimated by least squares, we know from wikipedia that $$ \hat {\beta} = {\rm cor}(Y_i, X_i) \cdot | How does the correlation coefficient differ from regression slope?
Assuming you're talking about a simple regression model $$Y_i = \alpha + \beta X_i + \varepsilon_i$$ estimated by least squares, we know from wikipedia that $$ \hat {\beta} = {\rm cor}(Y_i, X_i) \cdot \frac{ {\rm SD}(Y_i) }{ {\rm SD}(X_i) } $$ Therefore... | How does the correlation coefficient differ from regression slope?
Assuming you're talking about a simple regression model $$Y_i = \alpha + \beta X_i + \varepsilon_i$$ estimated by least squares, we know from wikipedia that $$ \hat {\beta} = {\rm cor}(Y_i, X_i) \cdot |
1,714 | How does the correlation coefficient differ from regression slope? | With simple linear regression (i.e., only 1 covariate), the slope $\beta_1$ is the same as Pearson's $r$ if both variables were standardized first. (For more information, you might find my answer here helpful.) When you are doing multiple regression, this can be more complicated due to multicollinearity, etc. | How does the correlation coefficient differ from regression slope? | With simple linear regression (i.e., only 1 covariate), the slope $\beta_1$ is the same as Pearson's $r$ if both variables were standardized first. (For more information, you might find my answer her | How does the correlation coefficient differ from regression slope?
With simple linear regression (i.e., only 1 covariate), the slope $\beta_1$ is the same as Pearson's $r$ if both variables were standardized first. (For more information, you might find my answer here helpful.) When you are doing multiple regression, ... | How does the correlation coefficient differ from regression slope?
With simple linear regression (i.e., only 1 covariate), the slope $\beta_1$ is the same as Pearson's $r$ if both variables were standardized first. (For more information, you might find my answer her |
1,715 | How does the correlation coefficient differ from regression slope? | The correlation coefficient measures the "tightness" of linear relationship between two variables and is bounded between -1 and 1, inclusive. Correlations close to zero represent no linear association between the variables, whereas correlations close to -1 or +1 indicate strong linear relationship. Intuitively, the e... | How does the correlation coefficient differ from regression slope? | The correlation coefficient measures the "tightness" of linear relationship between two variables and is bounded between -1 and 1, inclusive. Correlations close to zero represent no linear associatio | How does the correlation coefficient differ from regression slope?
The correlation coefficient measures the "tightness" of linear relationship between two variables and is bounded between -1 and 1, inclusive. Correlations close to zero represent no linear association between the variables, whereas correlations close t... | How does the correlation coefficient differ from regression slope?
The correlation coefficient measures the "tightness" of linear relationship between two variables and is bounded between -1 and 1, inclusive. Correlations close to zero represent no linear associatio |
1,716 | How does the correlation coefficient differ from regression slope? | Pearson's correlation coefficient is dimensionless and scaled between -1 and 1 regardless of the dimension and scale of the input variables.
If (for example) you input a mass in grams or kilograms, it makes no difference to the value of $r$, whereas this will make a tremendous difference to the gradient/slope (which h... | How does the correlation coefficient differ from regression slope? | Pearson's correlation coefficient is dimensionless and scaled between -1 and 1 regardless of the dimension and scale of the input variables.
If (for example) you input a mass in grams or kilograms, i | How does the correlation coefficient differ from regression slope?
Pearson's correlation coefficient is dimensionless and scaled between -1 and 1 regardless of the dimension and scale of the input variables.
If (for example) you input a mass in grams or kilograms, it makes no difference to the value of $r$, whereas th... | How does the correlation coefficient differ from regression slope?
Pearson's correlation coefficient is dimensionless and scaled between -1 and 1 regardless of the dimension and scale of the input variables.
If (for example) you input a mass in grams or kilograms, i |
1,717 | What is the benefit of breaking up a continuous predictor variable? | You're right on both counts. See Frank Harrell's page here for a long list of problems with binning continuous variables. If you use a few bins you throw away a lot of information in the predictors; if you use many you tend to fit wiggles in what should be a smooth, if not linear, relationship, & use up a lot of degree... | What is the benefit of breaking up a continuous predictor variable? | You're right on both counts. See Frank Harrell's page here for a long list of problems with binning continuous variables. If you use a few bins you throw away a lot of information in the predictors; i | What is the benefit of breaking up a continuous predictor variable?
You're right on both counts. See Frank Harrell's page here for a long list of problems with binning continuous variables. If you use a few bins you throw away a lot of information in the predictors; if you use many you tend to fit wiggles in what shoul... | What is the benefit of breaking up a continuous predictor variable?
You're right on both counts. See Frank Harrell's page here for a long list of problems with binning continuous variables. If you use a few bins you throw away a lot of information in the predictors; i |
1,718 | What is the benefit of breaking up a continuous predictor variable? | A part of this answer that I've learned since asking is that not binning and binning seeks to answer two slightly different questions - What is the incremental change in the data? and What is the difference between the lowest and the highest?.
Not binning says "this is a quantification of the trend seen in the data" a... | What is the benefit of breaking up a continuous predictor variable? | A part of this answer that I've learned since asking is that not binning and binning seeks to answer two slightly different questions - What is the incremental change in the data? and What is the diff | What is the benefit of breaking up a continuous predictor variable?
A part of this answer that I've learned since asking is that not binning and binning seeks to answer two slightly different questions - What is the incremental change in the data? and What is the difference between the lowest and the highest?.
Not bin... | What is the benefit of breaking up a continuous predictor variable?
A part of this answer that I've learned since asking is that not binning and binning seeks to answer two slightly different questions - What is the incremental change in the data? and What is the diff |
1,719 | What is the benefit of breaking up a continuous predictor variable? | As previous posters have mentioned, it is generally best to avoid dichotomizing a continuous variable. However, in answer to your question, there are instances where dichotomizing a continuous variable does confer advantages.
For instance, if a given variable contains missing values for a significant proportion of the... | What is the benefit of breaking up a continuous predictor variable? | As previous posters have mentioned, it is generally best to avoid dichotomizing a continuous variable. However, in answer to your question, there are instances where dichotomizing a continuous variabl | What is the benefit of breaking up a continuous predictor variable?
As previous posters have mentioned, it is generally best to avoid dichotomizing a continuous variable. However, in answer to your question, there are instances where dichotomizing a continuous variable does confer advantages.
For instance, if a given ... | What is the benefit of breaking up a continuous predictor variable?
As previous posters have mentioned, it is generally best to avoid dichotomizing a continuous variable. However, in answer to your question, there are instances where dichotomizing a continuous variabl |
1,720 | What is the benefit of breaking up a continuous predictor variable? | As a clinician I think the answer depends on what you want to do. If you want to make the best fit or make the best adjustment you can use continuous and squared variables.
If you want to describe and communicate complicated associations for a non-statistically oriented audience the use of categorised variables is be... | What is the benefit of breaking up a continuous predictor variable? | As a clinician I think the answer depends on what you want to do. If you want to make the best fit or make the best adjustment you can use continuous and squared variables.
If you want to describe a | What is the benefit of breaking up a continuous predictor variable?
As a clinician I think the answer depends on what you want to do. If you want to make the best fit or make the best adjustment you can use continuous and squared variables.
If you want to describe and communicate complicated associations for a non-st... | What is the benefit of breaking up a continuous predictor variable?
As a clinician I think the answer depends on what you want to do. If you want to make the best fit or make the best adjustment you can use continuous and squared variables.
If you want to describe a |
1,721 | What is the benefit of breaking up a continuous predictor variable? | Many times binning continuous variables comes with an uneasy feeling of causing damage due to information lost.
However, not only that you can bound the information loss, you can gain information and get more advantages.
If you use binning and get categorised variables you might be able to apply learning algorithms tha... | What is the benefit of breaking up a continuous predictor variable? | Many times binning continuous variables comes with an uneasy feeling of causing damage due to information lost.
However, not only that you can bound the information loss, you can gain information and | What is the benefit of breaking up a continuous predictor variable?
Many times binning continuous variables comes with an uneasy feeling of causing damage due to information lost.
However, not only that you can bound the information loss, you can gain information and get more advantages.
If you use binning and get cate... | What is the benefit of breaking up a continuous predictor variable?
Many times binning continuous variables comes with an uneasy feeling of causing damage due to information lost.
However, not only that you can bound the information loss, you can gain information and |
1,722 | What is the benefit of breaking up a continuous predictor variable? | If a variable has an effect at a specific threshold, create a new variable by binning it is a good thing to do. I always keep both variables, original one and binning one, and check which variable is a better predictor. | What is the benefit of breaking up a continuous predictor variable? | If a variable has an effect at a specific threshold, create a new variable by binning it is a good thing to do. I always keep both variables, original one and binning one, and check which variable is | What is the benefit of breaking up a continuous predictor variable?
If a variable has an effect at a specific threshold, create a new variable by binning it is a good thing to do. I always keep both variables, original one and binning one, and check which variable is a better predictor. | What is the benefit of breaking up a continuous predictor variable?
If a variable has an effect at a specific threshold, create a new variable by binning it is a good thing to do. I always keep both variables, original one and binning one, and check which variable is |
1,723 | What is the benefit of breaking up a continuous predictor variable? | I'm a committed fan of Frank Harrell's advice that analysts should resist premature discretization of continuous data. And I have several answers on CV and SO that demonstrate how to visualize interactions between continuous variables, since I think that is an even more valuable line of investigation. However, I also ... | What is the benefit of breaking up a continuous predictor variable? | I'm a committed fan of Frank Harrell's advice that analysts should resist premature discretization of continuous data. And I have several answers on CV and SO that demonstrate how to visualize interac | What is the benefit of breaking up a continuous predictor variable?
I'm a committed fan of Frank Harrell's advice that analysts should resist premature discretization of continuous data. And I have several answers on CV and SO that demonstrate how to visualize interactions between continuous variables, since I think th... | What is the benefit of breaking up a continuous predictor variable?
I'm a committed fan of Frank Harrell's advice that analysts should resist premature discretization of continuous data. And I have several answers on CV and SO that demonstrate how to visualize interac |
1,724 | What is the benefit of breaking up a continuous predictor variable? | I just want to add something to the discussion: Normally, I would tend to also not binning the predictor variables, as I've learned that loosing information is not much appreciated, and sometimes dangerous.
However, thinking of a massive amount of data, the performance of getting the required outcome could be something... | What is the benefit of breaking up a continuous predictor variable? | I just want to add something to the discussion: Normally, I would tend to also not binning the predictor variables, as I've learned that loosing information is not much appreciated, and sometimes dang | What is the benefit of breaking up a continuous predictor variable?
I just want to add something to the discussion: Normally, I would tend to also not binning the predictor variables, as I've learned that loosing information is not much appreciated, and sometimes dangerous.
However, thinking of a massive amount of data... | What is the benefit of breaking up a continuous predictor variable?
I just want to add something to the discussion: Normally, I would tend to also not binning the predictor variables, as I've learned that loosing information is not much appreciated, and sometimes dang |
1,725 | Feature selection and cross-validation | If you perform feature selection on all of the data and then cross-validate, then the test data in each fold of the cross-validation procedure was also used to choose the features and this is what biases the performance analysis.
Consider this example. We generate some target data by flipping a coin 10 times and recor... | Feature selection and cross-validation | If you perform feature selection on all of the data and then cross-validate, then the test data in each fold of the cross-validation procedure was also used to choose the features and this is what bia | Feature selection and cross-validation
If you perform feature selection on all of the data and then cross-validate, then the test data in each fold of the cross-validation procedure was also used to choose the features and this is what biases the performance analysis.
Consider this example. We generate some target dat... | Feature selection and cross-validation
If you perform feature selection on all of the data and then cross-validate, then the test data in each fold of the cross-validation procedure was also used to choose the features and this is what bia |
1,726 | Feature selection and cross-validation | To add a slightly different and more general description of the problem:
If you do any kind of data-driven pre-processing, e.g.
parameter optimization guided by cross validation / out-of-bootstrap
dimensionality reduction with techniques like PCA or PLS to produce input for the model (e.g. PLS-LDA, PCA-LDA)
...
and w... | Feature selection and cross-validation | To add a slightly different and more general description of the problem:
If you do any kind of data-driven pre-processing, e.g.
parameter optimization guided by cross validation / out-of-bootstrap
di | Feature selection and cross-validation
To add a slightly different and more general description of the problem:
If you do any kind of data-driven pre-processing, e.g.
parameter optimization guided by cross validation / out-of-bootstrap
dimensionality reduction with techniques like PCA or PLS to produce input for the m... | Feature selection and cross-validation
To add a slightly different and more general description of the problem:
If you do any kind of data-driven pre-processing, e.g.
parameter optimization guided by cross validation / out-of-bootstrap
di |
1,727 | Feature selection and cross-validation | Let's try to make it a little bit intuitive. Consider this example: You have a binary dependent and two binary predictors. You want a model with just one predictors. Both predictors have a chance of say 95% to be equal to the dependent and a chance of 5% to disagree with the dependent.
Now, by chance on your data one p... | Feature selection and cross-validation | Let's try to make it a little bit intuitive. Consider this example: You have a binary dependent and two binary predictors. You want a model with just one predictors. Both predictors have a chance of s | Feature selection and cross-validation
Let's try to make it a little bit intuitive. Consider this example: You have a binary dependent and two binary predictors. You want a model with just one predictors. Both predictors have a chance of say 95% to be equal to the dependent and a chance of 5% to disagree with the depen... | Feature selection and cross-validation
Let's try to make it a little bit intuitive. Consider this example: You have a binary dependent and two binary predictors. You want a model with just one predictors. Both predictors have a chance of s |
1,728 | What book would you recommend for non-statistician scientists? [closed] | Statistics
David Freedman, Robert Pisani, Roger Purves
Fourth edition: 2007, First edition: 1978
As an undergraduate studying philosophy, I was asked to analyze some data for a small study that I was working on with a physician. Needless to say, I found myself somewhat overwhelmed, but was able to get by by mimicking s... | What book would you recommend for non-statistician scientists? [closed] | Statistics
David Freedman, Robert Pisani, Roger Purves
Fourth edition: 2007, First edition: 1978
As an undergraduate studying philosophy, I was asked to analyze some data for a small study that I was | What book would you recommend for non-statistician scientists? [closed]
Statistics
David Freedman, Robert Pisani, Roger Purves
Fourth edition: 2007, First edition: 1978
As an undergraduate studying philosophy, I was asked to analyze some data for a small study that I was working on with a physician. Needless to say, I ... | What book would you recommend for non-statistician scientists? [closed]
Statistics
David Freedman, Robert Pisani, Roger Purves
Fourth edition: 2007, First edition: 1978
As an undergraduate studying philosophy, I was asked to analyze some data for a small study that I was |
1,729 | What book would you recommend for non-statistician scientists? [closed] | The answer would most definitely depend on their discipline, the methods/techniques that they would like to learn and their existing mathematical/statistical abilities.
For example, economists/social scientists who want to learn about cutting edge empirical econometrics could read Angrist and Pischke's Mostly Harmless ... | What book would you recommend for non-statistician scientists? [closed] | The answer would most definitely depend on their discipline, the methods/techniques that they would like to learn and their existing mathematical/statistical abilities.
For example, economists/social | What book would you recommend for non-statistician scientists? [closed]
The answer would most definitely depend on their discipline, the methods/techniques that they would like to learn and their existing mathematical/statistical abilities.
For example, economists/social scientists who want to learn about cutting edge ... | What book would you recommend for non-statistician scientists? [closed]
The answer would most definitely depend on their discipline, the methods/techniques that they would like to learn and their existing mathematical/statistical abilities.
For example, economists/social |
1,730 | What book would you recommend for non-statistician scientists? [closed] | Peter Dalgaard's Introductory Statistics with R is a great book for some introductory statistics with a focus on the R software for data analysis. | What book would you recommend for non-statistician scientists? [closed] | Peter Dalgaard's Introductory Statistics with R is a great book for some introductory statistics with a focus on the R software for data analysis. | What book would you recommend for non-statistician scientists? [closed]
Peter Dalgaard's Introductory Statistics with R is a great book for some introductory statistics with a focus on the R software for data analysis. | What book would you recommend for non-statistician scientists? [closed]
Peter Dalgaard's Introductory Statistics with R is a great book for some introductory statistics with a focus on the R software for data analysis. |
1,731 | What book would you recommend for non-statistician scientists? [closed] | I'm going to assume some basic statistics knowledge and recommend:
The Statistical Sleuth (Ramsey, Schafer) which contain a good deal of mini case studies as they cover the basic statistical tools for data analysis.
A First Course in Multivariate Statistics (Flury) which covers the essential statistics required for ... | What book would you recommend for non-statistician scientists? [closed] | I'm going to assume some basic statistics knowledge and recommend:
The Statistical Sleuth (Ramsey, Schafer) which contain a good deal of mini case studies as they cover the basic statistical tools fo | What book would you recommend for non-statistician scientists? [closed]
I'm going to assume some basic statistics knowledge and recommend:
The Statistical Sleuth (Ramsey, Schafer) which contain a good deal of mini case studies as they cover the basic statistical tools for data analysis.
A First Course in Multivariat... | What book would you recommend for non-statistician scientists? [closed]
I'm going to assume some basic statistics knowledge and recommend:
The Statistical Sleuth (Ramsey, Schafer) which contain a good deal of mini case studies as they cover the basic statistical tools fo |
1,732 | What book would you recommend for non-statistician scientists? [closed] | Khan Academy has some nice introductory/beginner videos on statistics. | What book would you recommend for non-statistician scientists? [closed] | Khan Academy has some nice introductory/beginner videos on statistics. | What book would you recommend for non-statistician scientists? [closed]
Khan Academy has some nice introductory/beginner videos on statistics. | What book would you recommend for non-statistician scientists? [closed]
Khan Academy has some nice introductory/beginner videos on statistics. |
1,733 | What book would you recommend for non-statistician scientists? [closed] | A lot of Social Science / Psychology students with minimal mathematical background like Andy Field's book: Discovering Statistics Using SPSS. He also has a website that shares a lot of material. | What book would you recommend for non-statistician scientists? [closed] | A lot of Social Science / Psychology students with minimal mathematical background like Andy Field's book: Discovering Statistics Using SPSS. He also has a website that shares a lot of material. | What book would you recommend for non-statistician scientists? [closed]
A lot of Social Science / Psychology students with minimal mathematical background like Andy Field's book: Discovering Statistics Using SPSS. He also has a website that shares a lot of material. | What book would you recommend for non-statistician scientists? [closed]
A lot of Social Science / Psychology students with minimal mathematical background like Andy Field's book: Discovering Statistics Using SPSS. He also has a website that shares a lot of material. |
1,734 | What book would you recommend for non-statistician scientists? [closed] | Not intending to plug my book but it does seem to possibly apply. Last year I published a book with Wiley titled "The Essentials of Biostatistics for Physicians, Nurses and Clinicians". It is paperback and fairly concise 214 pages in total. It has the advantage for you that it emphasizes topics that are important in... | What book would you recommend for non-statistician scientists? [closed] | Not intending to plug my book but it does seem to possibly apply. Last year I published a book with Wiley titled "The Essentials of Biostatistics for Physicians, Nurses and Clinicians". It is paperb | What book would you recommend for non-statistician scientists? [closed]
Not intending to plug my book but it does seem to possibly apply. Last year I published a book with Wiley titled "The Essentials of Biostatistics for Physicians, Nurses and Clinicians". It is paperback and fairly concise 214 pages in total. It h... | What book would you recommend for non-statistician scientists? [closed]
Not intending to plug my book but it does seem to possibly apply. Last year I published a book with Wiley titled "The Essentials of Biostatistics for Physicians, Nurses and Clinicians". It is paperb |
1,735 | What book would you recommend for non-statistician scientists? [closed] | Statistics in Plain English is pretty good.
4.5 on Amazon, 11 reviews.
Explains ANOVA pretty well too. | What book would you recommend for non-statistician scientists? [closed] | Statistics in Plain English is pretty good.
4.5 on Amazon, 11 reviews.
Explains ANOVA pretty well too. | What book would you recommend for non-statistician scientists? [closed]
Statistics in Plain English is pretty good.
4.5 on Amazon, 11 reviews.
Explains ANOVA pretty well too. | What book would you recommend for non-statistician scientists? [closed]
Statistics in Plain English is pretty good.
4.5 on Amazon, 11 reviews.
Explains ANOVA pretty well too. |
1,736 | What book would you recommend for non-statistician scientists? [closed] | Probably the best basic, get the big picture / ideas book is going to be:
Robert Abelson's Statistics as Principled Argument | What book would you recommend for non-statistician scientists? [closed] | Probably the best basic, get the big picture / ideas book is going to be:
Robert Abelson's Statistics as Principled Argument | What book would you recommend for non-statistician scientists? [closed]
Probably the best basic, get the big picture / ideas book is going to be:
Robert Abelson's Statistics as Principled Argument | What book would you recommend for non-statistician scientists? [closed]
Probably the best basic, get the big picture / ideas book is going to be:
Robert Abelson's Statistics as Principled Argument |
1,737 | What book would you recommend for non-statistician scientists? [closed] | The Drunkard's Walk: How Randomness Rules Our Lives by Leonard Mlodinow is an excellent book for laypeople. Enjoyable and educational.
It might not be a textbook, but it makes you think about the world in the right way. | What book would you recommend for non-statistician scientists? [closed] | The Drunkard's Walk: How Randomness Rules Our Lives by Leonard Mlodinow is an excellent book for laypeople. Enjoyable and educational.
It might not be a textbook, but it makes you think about the worl | What book would you recommend for non-statistician scientists? [closed]
The Drunkard's Walk: How Randomness Rules Our Lives by Leonard Mlodinow is an excellent book for laypeople. Enjoyable and educational.
It might not be a textbook, but it makes you think about the world in the right way. | What book would you recommend for non-statistician scientists? [closed]
The Drunkard's Walk: How Randomness Rules Our Lives by Leonard Mlodinow is an excellent book for laypeople. Enjoyable and educational.
It might not be a textbook, but it makes you think about the worl |
1,738 | What book would you recommend for non-statistician scientists? [closed] | It is a bit old, but I have found Chris Chatfield's book,
Statistics for Technology: A Course in Applied Technology
to be an excellent introduction.
It was how I first learned about statistics from a conceptual point of view. | What book would you recommend for non-statistician scientists? [closed] | It is a bit old, but I have found Chris Chatfield's book,
Statistics for Technology: A Course in Applied Technology
to be an excellent introduction.
It was how I first learned about statistics from a | What book would you recommend for non-statistician scientists? [closed]
It is a bit old, but I have found Chris Chatfield's book,
Statistics for Technology: A Course in Applied Technology
to be an excellent introduction.
It was how I first learned about statistics from a conceptual point of view. | What book would you recommend for non-statistician scientists? [closed]
It is a bit old, but I have found Chris Chatfield's book,
Statistics for Technology: A Course in Applied Technology
to be an excellent introduction.
It was how I first learned about statistics from a |
1,739 | What book would you recommend for non-statistician scientists? [closed] | As a first introduction to the topic i liked Data Analysis: A Bayesian Tutorial.
For a deep and philosophical discussion of the underlying ideas of quantitative scientific reasoning i recommend Probability Theory: The Logic of Science. This book does not serve as a good introduction, though. It's only recommended for p... | What book would you recommend for non-statistician scientists? [closed] | As a first introduction to the topic i liked Data Analysis: A Bayesian Tutorial.
For a deep and philosophical discussion of the underlying ideas of quantitative scientific reasoning i recommend Probab | What book would you recommend for non-statistician scientists? [closed]
As a first introduction to the topic i liked Data Analysis: A Bayesian Tutorial.
For a deep and philosophical discussion of the underlying ideas of quantitative scientific reasoning i recommend Probability Theory: The Logic of Science. This book do... | What book would you recommend for non-statistician scientists? [closed]
As a first introduction to the topic i liked Data Analysis: A Bayesian Tutorial.
For a deep and philosophical discussion of the underlying ideas of quantitative scientific reasoning i recommend Probab |
1,740 | What book would you recommend for non-statistician scientists? [closed] | So many wonderful recommendations! It's not quite what you asked for, but How to Lie with Statistics is short and quite wonderful. It doesn't directly teach the things you want, but it does help point out violation of assumptions and other flaws. | What book would you recommend for non-statistician scientists? [closed] | So many wonderful recommendations! It's not quite what you asked for, but How to Lie with Statistics is short and quite wonderful. It doesn't directly teach the things you want, but it does help point | What book would you recommend for non-statistician scientists? [closed]
So many wonderful recommendations! It's not quite what you asked for, but How to Lie with Statistics is short and quite wonderful. It doesn't directly teach the things you want, but it does help point out violation of assumptions and other flaws. | What book would you recommend for non-statistician scientists? [closed]
So many wonderful recommendations! It's not quite what you asked for, but How to Lie with Statistics is short and quite wonderful. It doesn't directly teach the things you want, but it does help point |
1,741 | What book would you recommend for non-statistician scientists? [closed] | The Flaw of Averages by Sam Savage. | What book would you recommend for non-statistician scientists? [closed] | The Flaw of Averages by Sam Savage. | What book would you recommend for non-statistician scientists? [closed]
The Flaw of Averages by Sam Savage. | What book would you recommend for non-statistician scientists? [closed]
The Flaw of Averages by Sam Savage. |
1,742 | What book would you recommend for non-statistician scientists? [closed] | "How to Tell the Liars from the Statisticians" by Hooke. I am fond of its way of explaining the concepts of statistics to laypersons.
As for explaining the motivations of statisticians, "The Lady Tasting Tea" is good reading. | What book would you recommend for non-statistician scientists? [closed] | "How to Tell the Liars from the Statisticians" by Hooke. I am fond of its way of explaining the concepts of statistics to laypersons.
As for explaining the motivations of statisticians, "The Lady Tast | What book would you recommend for non-statistician scientists? [closed]
"How to Tell the Liars from the Statisticians" by Hooke. I am fond of its way of explaining the concepts of statistics to laypersons.
As for explaining the motivations of statisticians, "The Lady Tasting Tea" is good reading. | What book would you recommend for non-statistician scientists? [closed]
"How to Tell the Liars from the Statisticians" by Hooke. I am fond of its way of explaining the concepts of statistics to laypersons.
As for explaining the motivations of statisticians, "The Lady Tast |
1,743 | What book would you recommend for non-statistician scientists? [closed] | "Biometry: The Principles and Practices of Statistics in Biological Research" by Robert R. Sokal and F. James Rohlf
"Biostatistical Analysis" by Jerrold H. Zar
"Primer of Biostatistics" by Stanton Glantz | What book would you recommend for non-statistician scientists? [closed] | "Biometry: The Principles and Practices of Statistics in Biological Research" by Robert R. Sokal and F. James Rohlf
"Biostatistical Analysis" by Jerrold H. Zar
"Primer of Biostatistics" by Stanton Gla | What book would you recommend for non-statistician scientists? [closed]
"Biometry: The Principles and Practices of Statistics in Biological Research" by Robert R. Sokal and F. James Rohlf
"Biostatistical Analysis" by Jerrold H. Zar
"Primer of Biostatistics" by Stanton Glantz | What book would you recommend for non-statistician scientists? [closed]
"Biometry: The Principles and Practices of Statistics in Biological Research" by Robert R. Sokal and F. James Rohlf
"Biostatistical Analysis" by Jerrold H. Zar
"Primer of Biostatistics" by Stanton Gla |
1,744 | What book would you recommend for non-statistician scientists? [closed] | For the rudiments of statistics: http://www.bbc.co.uk/dna/h2g2/A1091350 and http://www.robertniles.com/stats/
For a good guide to data visualisation: http://www.perceptualedge.com/ - in particular, try the Graph Design IQ test at http://www.perceptualedge.com/files/GraphDesignIQ.html (requires Flash)
NB these are ortho... | What book would you recommend for non-statistician scientists? [closed] | For the rudiments of statistics: http://www.bbc.co.uk/dna/h2g2/A1091350 and http://www.robertniles.com/stats/
For a good guide to data visualisation: http://www.perceptualedge.com/ - in particular, tr | What book would you recommend for non-statistician scientists? [closed]
For the rudiments of statistics: http://www.bbc.co.uk/dna/h2g2/A1091350 and http://www.robertniles.com/stats/
For a good guide to data visualisation: http://www.perceptualedge.com/ - in particular, try the Graph Design IQ test at http://www.percept... | What book would you recommend for non-statistician scientists? [closed]
For the rudiments of statistics: http://www.bbc.co.uk/dna/h2g2/A1091350 and http://www.robertniles.com/stats/
For a good guide to data visualisation: http://www.perceptualedge.com/ - in particular, tr |
1,745 | What book would you recommend for non-statistician scientists? [closed] | The following are text books I used for my MSEE coursework and research and I found them to be pretty good.
Probability, Statistics and Random Processes for Engineers by Henry Stark and John W. Woods
(Detailed explanation of concepts, good for Communications and Signal Processing people).
Schaum's Outline of Probabil... | What book would you recommend for non-statistician scientists? [closed] | The following are text books I used for my MSEE coursework and research and I found them to be pretty good.
Probability, Statistics and Random Processes for Engineers by Henry Stark and John W. Wood | What book would you recommend for non-statistician scientists? [closed]
The following are text books I used for my MSEE coursework and research and I found them to be pretty good.
Probability, Statistics and Random Processes for Engineers by Henry Stark and John W. Woods
(Detailed explanation of concepts, good for Co... | What book would you recommend for non-statistician scientists? [closed]
The following are text books I used for my MSEE coursework and research and I found them to be pretty good.
Probability, Statistics and Random Processes for Engineers by Henry Stark and John W. Wood |
1,746 | What book would you recommend for non-statistician scientists? [closed] | I recently found Even You Can Learn Statistics to be pretty useful. | What book would you recommend for non-statistician scientists? [closed] | I recently found Even You Can Learn Statistics to be pretty useful. | What book would you recommend for non-statistician scientists? [closed]
I recently found Even You Can Learn Statistics to be pretty useful. | What book would you recommend for non-statistician scientists? [closed]
I recently found Even You Can Learn Statistics to be pretty useful. |
1,747 | What book would you recommend for non-statistician scientists? [closed] | I strongly recommend "Statistics for Experimenters: Design, Innovation, and Discovery , 2nd Edition" by Box, Hunter and Hunter. Must-read book for any scientist doing statistical analysis of their experiments. There's a companion R package (BHH2) as well. | What book would you recommend for non-statistician scientists? [closed] | I strongly recommend "Statistics for Experimenters: Design, Innovation, and Discovery , 2nd Edition" by Box, Hunter and Hunter. Must-read book for any scientist doing statistical analysis of their exp | What book would you recommend for non-statistician scientists? [closed]
I strongly recommend "Statistics for Experimenters: Design, Innovation, and Discovery , 2nd Edition" by Box, Hunter and Hunter. Must-read book for any scientist doing statistical analysis of their experiments. There's a companion R package (BHH2) a... | What book would you recommend for non-statistician scientists? [closed]
I strongly recommend "Statistics for Experimenters: Design, Innovation, and Discovery , 2nd Edition" by Box, Hunter and Hunter. Must-read book for any scientist doing statistical analysis of their exp |
1,748 | What book would you recommend for non-statistician scientists? [closed] | For years I have found the Engineering Statistics Handbook to be useful on a practical level.
It's freely available online. | What book would you recommend for non-statistician scientists? [closed] | For years I have found the Engineering Statistics Handbook to be useful on a practical level.
It's freely available online. | What book would you recommend for non-statistician scientists? [closed]
For years I have found the Engineering Statistics Handbook to be useful on a practical level.
It's freely available online. | What book would you recommend for non-statistician scientists? [closed]
For years I have found the Engineering Statistics Handbook to be useful on a practical level.
It's freely available online. |
1,749 | What book would you recommend for non-statistician scientists? [closed] | Gotelli and Ellison (2004) A Primer of Ecological Statistics
It's geared towards "Outdoor Science" (Ecology, Environmental Science, Biology) but the pedagogy is excellent. Anyone could benefit from it. | What book would you recommend for non-statistician scientists? [closed] | Gotelli and Ellison (2004) A Primer of Ecological Statistics
It's geared towards "Outdoor Science" (Ecology, Environmental Science, Biology) but the pedagogy is excellent. Anyone could benefit from it | What book would you recommend for non-statistician scientists? [closed]
Gotelli and Ellison (2004) A Primer of Ecological Statistics
It's geared towards "Outdoor Science" (Ecology, Environmental Science, Biology) but the pedagogy is excellent. Anyone could benefit from it. | What book would you recommend for non-statistician scientists? [closed]
Gotelli and Ellison (2004) A Primer of Ecological Statistics
It's geared towards "Outdoor Science" (Ecology, Environmental Science, Biology) but the pedagogy is excellent. Anyone could benefit from it |
1,750 | What book would you recommend for non-statistician scientists? [closed] | Whitlock and Schluter The Analysis of Biological Data
3rd edition 2020 details at
https://www.amazon.com/Analysis-Biological-Data-Michael-Whitlock/dp/131922623X
is an outstanding blend of statistics and science. You don't have to be a biologist (I'm certainly not) to understand and appreciate the examples. It's not onl... | What book would you recommend for non-statistician scientists? [closed] | Whitlock and Schluter The Analysis of Biological Data
3rd edition 2020 details at
https://www.amazon.com/Analysis-Biological-Data-Michael-Whitlock/dp/131922623X
is an outstanding blend of statistics a | What book would you recommend for non-statistician scientists? [closed]
Whitlock and Schluter The Analysis of Biological Data
3rd edition 2020 details at
https://www.amazon.com/Analysis-Biological-Data-Michael-Whitlock/dp/131922623X
is an outstanding blend of statistics and science. You don't have to be a biologist (I'... | What book would you recommend for non-statistician scientists? [closed]
Whitlock and Schluter The Analysis of Biological Data
3rd edition 2020 details at
https://www.amazon.com/Analysis-Biological-Data-Michael-Whitlock/dp/131922623X
is an outstanding blend of statistics a |
1,751 | What book would you recommend for non-statistician scientists? [closed] | I have recently had this website pointed out to me. It covers a number of books useful for new statisticians, with some targetted discussion of their various strengths and weaknesses, and a summary right at the bottom. | What book would you recommend for non-statistician scientists? [closed] | I have recently had this website pointed out to me. It covers a number of books useful for new statisticians, with some targetted discussion of their various strengths and weaknesses, and a summary ri | What book would you recommend for non-statistician scientists? [closed]
I have recently had this website pointed out to me. It covers a number of books useful for new statisticians, with some targetted discussion of their various strengths and weaknesses, and a summary right at the bottom. | What book would you recommend for non-statistician scientists? [closed]
I have recently had this website pointed out to me. It covers a number of books useful for new statisticians, with some targetted discussion of their various strengths and weaknesses, and a summary ri |
1,752 | What book would you recommend for non-statistician scientists? [closed] | "Theoretical Statistics"
Keener, Robert W.
1st Edition., 2010, XVII, 538 p.
Hardcover, ISBN 978-0-387-93838-7
About the book... | What book would you recommend for non-statistician scientists? [closed] | "Theoretical Statistics"
Keener, Robert W.
1st Edition., 2010, XVII, 538 p.
Hardcover, ISBN 978-0-387-93838-7
About the book... | What book would you recommend for non-statistician scientists? [closed]
"Theoretical Statistics"
Keener, Robert W.
1st Edition., 2010, XVII, 538 p.
Hardcover, ISBN 978-0-387-93838-7
About the book... | What book would you recommend for non-statistician scientists? [closed]
"Theoretical Statistics"
Keener, Robert W.
1st Edition., 2010, XVII, 538 p.
Hardcover, ISBN 978-0-387-93838-7
About the book... |
1,753 | What book would you recommend for non-statistician scientists? [closed] | I would recommend: The statistical sleuth (Ramsey&Schafer) and biostatistical analysis (Zar). | What book would you recommend for non-statistician scientists? [closed] | I would recommend: The statistical sleuth (Ramsey&Schafer) and biostatistical analysis (Zar). | What book would you recommend for non-statistician scientists? [closed]
I would recommend: The statistical sleuth (Ramsey&Schafer) and biostatistical analysis (Zar). | What book would you recommend for non-statistician scientists? [closed]
I would recommend: The statistical sleuth (Ramsey&Schafer) and biostatistical analysis (Zar). |
1,754 | What book would you recommend for non-statistician scientists? [closed] | I'm really fond of the "for Dummies" series, and from the few pages I've read of it, Deborah J. Rumsey's "Statistics For Dummies" is a fine book for non-statisticians as well as Statisticians looking for a way to explain statistical concepts to non-statisticians. | What book would you recommend for non-statistician scientists? [closed] | I'm really fond of the "for Dummies" series, and from the few pages I've read of it, Deborah J. Rumsey's "Statistics For Dummies" is a fine book for non-statisticians as well as Statisticians looking | What book would you recommend for non-statistician scientists? [closed]
I'm really fond of the "for Dummies" series, and from the few pages I've read of it, Deborah J. Rumsey's "Statistics For Dummies" is a fine book for non-statisticians as well as Statisticians looking for a way to explain statistical concepts to non... | What book would you recommend for non-statistician scientists? [closed]
I'm really fond of the "for Dummies" series, and from the few pages I've read of it, Deborah J. Rumsey's "Statistics For Dummies" is a fine book for non-statisticians as well as Statisticians looking |
1,755 | What book would you recommend for non-statistician scientists? [closed] | This link suggested many great books.
https://www.stat.berkeley.edu/mediawiki/index.php/Recommended_Books
besides that, I suggested: The Statistical Sleuth: A Course in Methods of Data Analysis. Following the examples in the book, many concepts become easier to understand. | What book would you recommend for non-statistician scientists? [closed] | This link suggested many great books.
https://www.stat.berkeley.edu/mediawiki/index.php/Recommended_Books
besides that, I suggested: The Statistical Sleuth: A Course in Methods of Data Analysis. Follo | What book would you recommend for non-statistician scientists? [closed]
This link suggested many great books.
https://www.stat.berkeley.edu/mediawiki/index.php/Recommended_Books
besides that, I suggested: The Statistical Sleuth: A Course in Methods of Data Analysis. Following the examples in the book, many concepts bec... | What book would you recommend for non-statistician scientists? [closed]
This link suggested many great books.
https://www.stat.berkeley.edu/mediawiki/index.php/Recommended_Books
besides that, I suggested: The Statistical Sleuth: A Course in Methods of Data Analysis. Follo |
1,756 | What book would you recommend for non-statistician scientists? [closed] | If you're to use SPSS, I'd recommend this book: Data Analysis for the Behavioral Sciences Using SPSS by Weinberg & Abramowitz. It is very well written and accessible. Note that it doesn't cover time-series, though. | What book would you recommend for non-statistician scientists? [closed] | If you're to use SPSS, I'd recommend this book: Data Analysis for the Behavioral Sciences Using SPSS by Weinberg & Abramowitz. It is very well written and accessible. Note that it doesn't cover time-s | What book would you recommend for non-statistician scientists? [closed]
If you're to use SPSS, I'd recommend this book: Data Analysis for the Behavioral Sciences Using SPSS by Weinberg & Abramowitz. It is very well written and accessible. Note that it doesn't cover time-series, though. | What book would you recommend for non-statistician scientists? [closed]
If you're to use SPSS, I'd recommend this book: Data Analysis for the Behavioral Sciences Using SPSS by Weinberg & Abramowitz. It is very well written and accessible. Note that it doesn't cover time-s |
1,757 | What book would you recommend for non-statistician scientists? [closed] | That'll depend very much on their background, but I found "Statistics in a Nutshell" to be pretty good. | What book would you recommend for non-statistician scientists? [closed] | That'll depend very much on their background, but I found "Statistics in a Nutshell" to be pretty good. | What book would you recommend for non-statistician scientists? [closed]
That'll depend very much on their background, but I found "Statistics in a Nutshell" to be pretty good. | What book would you recommend for non-statistician scientists? [closed]
That'll depend very much on their background, but I found "Statistics in a Nutshell" to be pretty good. |
1,758 | Correlation between a nominal (IV) and a continuous (DV) variable | The title of this question suggests a fundamental misunderstanding. The most basic idea of correlation is "as one variable increases, does the other variable increase (positive correlation), decrease (negative correlation), or stay the same (no correlation)" with a scale such that perfect positive correlation is +1, no... | Correlation between a nominal (IV) and a continuous (DV) variable | The title of this question suggests a fundamental misunderstanding. The most basic idea of correlation is "as one variable increases, does the other variable increase (positive correlation), decrease | Correlation between a nominal (IV) and a continuous (DV) variable
The title of this question suggests a fundamental misunderstanding. The most basic idea of correlation is "as one variable increases, does the other variable increase (positive correlation), decrease (negative correlation), or stay the same (no correlati... | Correlation between a nominal (IV) and a continuous (DV) variable
The title of this question suggests a fundamental misunderstanding. The most basic idea of correlation is "as one variable increases, does the other variable increase (positive correlation), decrease |
1,759 | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | Consider the simplest case where $Y$ is regressed against $X$ and $Z$ and where $X$ and $Z$ are highly positively correlated. Then the effect of $X$ on $Y$ is hard to distinguish from the effect of $Z$ on $Y$ because any increase in $X$ tends to be associated with an increase in $Z$.
Another way to look at this is to ... | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | Consider the simplest case where $Y$ is regressed against $X$ and $Z$ and where $X$ and $Z$ are highly positively correlated. Then the effect of $X$ on $Y$ is hard to distinguish from the effect of $Z | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
Consider the simplest case where $Y$ is regressed against $X$ and $Z$ and where $X$ and $Z$ are highly positively correlated. Then the effect of $X$ on $Y$ is hard to distinguish from the effect of $Z$ on $Y$ because any increase... | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
Consider the simplest case where $Y$ is regressed against $X$ and $Z$ and where $X$ and $Z$ are highly positively correlated. Then the effect of $X$ on $Y$ is hard to distinguish from the effect of $Z |
1,760 | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | I was eating sushi once and thought that it might make a good intuitive demonstration of ill-conditioned problems. Suppose you wanted to show someone a plane using two sticks touching at their bases.
You'd probably hold the sticks orthogonal to each other. The effect of any kind of shakiness of your hands on the pla... | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | I was eating sushi once and thought that it might make a good intuitive demonstration of ill-conditioned problems. Suppose you wanted to show someone a plane using two sticks touching at their bases. | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
I was eating sushi once and thought that it might make a good intuitive demonstration of ill-conditioned problems. Suppose you wanted to show someone a plane using two sticks touching at their bases.
You'd probably hold the sti... | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
I was eating sushi once and thought that it might make a good intuitive demonstration of ill-conditioned problems. Suppose you wanted to show someone a plane using two sticks touching at their bases. |
1,761 | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | The geometric approach is to consider the least squares projection of $Y$ onto the subspace spanned by $X$.
Say you have a model:
$E[Y | X] = \beta_{1} X_{1} + \beta_{2} X_{2}$
Our estimation space is the plane determined by the vectors $X_{1}$ and $X_{2}$ and the problem is to find coordinates corresponding to $(\beta... | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | The geometric approach is to consider the least squares projection of $Y$ onto the subspace spanned by $X$.
Say you have a model:
$E[Y | X] = \beta_{1} X_{1} + \beta_{2} X_{2}$
Our estimation space is | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
The geometric approach is to consider the least squares projection of $Y$ onto the subspace spanned by $X$.
Say you have a model:
$E[Y | X] = \beta_{1} X_{1} + \beta_{2} X_{2}$
Our estimation space is the plane determined by the ... | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
The geometric approach is to consider the least squares projection of $Y$ onto the subspace spanned by $X$.
Say you have a model:
$E[Y | X] = \beta_{1} X_{1} + \beta_{2} X_{2}$
Our estimation space is |
1,762 | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | Two people are pushing a boulder up a hill. You want to know how hard each of them is pushing. Suppose that you watch them push together for ten minutes and the boulder moves 10 feet. Did the first guy do all the work and the second just fake it? Or vice versa? Or 50-50? Since both forces are working at the exact same ... | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | Two people are pushing a boulder up a hill. You want to know how hard each of them is pushing. Suppose that you watch them push together for ten minutes and the boulder moves 10 feet. Did the first gu | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
Two people are pushing a boulder up a hill. You want to know how hard each of them is pushing. Suppose that you watch them push together for ten minutes and the boulder moves 10 feet. Did the first guy do all the work and the sec... | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
Two people are pushing a boulder up a hill. You want to know how hard each of them is pushing. Suppose that you watch them push together for ten minutes and the boulder moves 10 feet. Did the first gu |
1,763 | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | The way I think about this really is in terms of information. Say each of $X_{1}$ and $X_{2}$ has some information about $Y$. The more correlated $X_{1}$ and $X_{2}$ are with each other, the more the information content about $Y$ from $X_{1}$ and $X_{2}$ are similar or overlapping, to the point that for perfectly corre... | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | The way I think about this really is in terms of information. Say each of $X_{1}$ and $X_{2}$ has some information about $Y$. The more correlated $X_{1}$ and $X_{2}$ are with each other, the more the | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
The way I think about this really is in terms of information. Say each of $X_{1}$ and $X_{2}$ has some information about $Y$. The more correlated $X_{1}$ and $X_{2}$ are with each other, the more the information content about $Y$... | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
The way I think about this really is in terms of information. Say each of $X_{1}$ and $X_{2}$ has some information about $Y$. The more correlated $X_{1}$ and $X_{2}$ are with each other, the more the |
1,764 | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | My (very) layman intuition for this is that the OLS model needs a certain level of "signal" in the X variable in order to detect it gives a "good" predicting for Y. If the same "signal" is spread over many X's (because they are correlated), then none of the correlated X's can give enough of a "proof" (statistical sign... | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | My (very) layman intuition for this is that the OLS model needs a certain level of "signal" in the X variable in order to detect it gives a "good" predicting for Y. If the same "signal" is spread ove | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
My (very) layman intuition for this is that the OLS model needs a certain level of "signal" in the X variable in order to detect it gives a "good" predicting for Y. If the same "signal" is spread over many X's (because they are ... | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
My (very) layman intuition for this is that the OLS model needs a certain level of "signal" in the X variable in order to detect it gives a "good" predicting for Y. If the same "signal" is spread ove |
1,765 | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | Assume that two people collaborated and accomplished scientific discovery.
It is easy to tell their unique contributions (who did what) when two are totally different persons (one is theory guy and the other is good at experiment), while it is difficult to distinguish their unique influences (coefficients in regression... | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | Assume that two people collaborated and accomplished scientific discovery.
It is easy to tell their unique contributions (who did what) when two are totally different persons (one is theory guy and th | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
Assume that two people collaborated and accomplished scientific discovery.
It is easy to tell their unique contributions (who did what) when two are totally different persons (one is theory guy and the other is good at experiment... | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
Assume that two people collaborated and accomplished scientific discovery.
It is easy to tell their unique contributions (who did what) when two are totally different persons (one is theory guy and th |
1,766 | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | If two regressors are perfectly correlated, their coefficients will be impossible to calculate; it's helpful to consider why they would be difficult to interpret if we could calculate them. In fact, this explains why it's difficult to interpret variables that are not perfectly correlated but that are also not truly in... | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | If two regressors are perfectly correlated, their coefficients will be impossible to calculate; it's helpful to consider why they would be difficult to interpret if we could calculate them. In fact, | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
If two regressors are perfectly correlated, their coefficients will be impossible to calculate; it's helpful to consider why they would be difficult to interpret if we could calculate them. In fact, this explains why it's diffic... | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
If two regressors are perfectly correlated, their coefficients will be impossible to calculate; it's helpful to consider why they would be difficult to interpret if we could calculate them. In fact, |
1,767 | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | I think the dummy variable trap provides another useful possibility to illustrate why multicollinearity is a problem. Recall that it arises when we have a constant and a full set of dummies in the model. Then, the sum of the dummies adds up to one, the constant, so multicollinearity.
E.g., a dummy for men and one for w... | Is there an intuitive explanation why multicollinearity is a problem in linear regression? | I think the dummy variable trap provides another useful possibility to illustrate why multicollinearity is a problem. Recall that it arises when we have a constant and a full set of dummies in the mod | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
I think the dummy variable trap provides another useful possibility to illustrate why multicollinearity is a problem. Recall that it arises when we have a constant and a full set of dummies in the model. Then, the sum of the dumm... | Is there an intuitive explanation why multicollinearity is a problem in linear regression?
I think the dummy variable trap provides another useful possibility to illustrate why multicollinearity is a problem. Recall that it arises when we have a constant and a full set of dummies in the mod |
1,768 | How scared should we be about convergence warnings in lme4 | Be afraid. Be very afraid.
Last year, I interviewed John Nash, the author of optim and optimx, for an article on IBM's DeveloperWorks site. We talked about how optimizers work and why they fail when they fail. He seemed to take it for granted that they often do. That's why the diagnostics are included in the package. ... | How scared should we be about convergence warnings in lme4 | Be afraid. Be very afraid.
Last year, I interviewed John Nash, the author of optim and optimx, for an article on IBM's DeveloperWorks site. We talked about how optimizers work and why they fail when | How scared should we be about convergence warnings in lme4
Be afraid. Be very afraid.
Last year, I interviewed John Nash, the author of optim and optimx, for an article on IBM's DeveloperWorks site. We talked about how optimizers work and why they fail when they fail. He seemed to take it for granted that they often d... | How scared should we be about convergence warnings in lme4
Be afraid. Be very afraid.
Last year, I interviewed John Nash, the author of optim and optimx, for an article on IBM's DeveloperWorks site. We talked about how optimizers work and why they fail when |
1,769 | How scared should we be about convergence warnings in lme4 | I just want to supplement @Placidia's great answer. You may want to check out "Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects" by James Hodges (2014). It discuses what we do not know about mixed models and at the same time attempts to offer a broad theory as well as p... | How scared should we be about convergence warnings in lme4 | I just want to supplement @Placidia's great answer. You may want to check out "Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects" by James Hodges (2014 | How scared should we be about convergence warnings in lme4
I just want to supplement @Placidia's great answer. You may want to check out "Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects" by James Hodges (2014). It discuses what we do not know about mixed models and at ... | How scared should we be about convergence warnings in lme4
I just want to supplement @Placidia's great answer. You may want to check out "Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects" by James Hodges (2014 |
1,770 | Conditional inference trees vs traditional decision trees | For what it's worth:
both rpart and ctree recursively perform univariate splits of the dependent variable based on values on a set of covariates. rpart and related algorithms usually employ information measures (such as the Gini coefficient) for selecting the current covariate.
ctree, according to its authors (see chl'... | Conditional inference trees vs traditional decision trees | For what it's worth:
both rpart and ctree recursively perform univariate splits of the dependent variable based on values on a set of covariates. rpart and related algorithms usually employ informatio | Conditional inference trees vs traditional decision trees
For what it's worth:
both rpart and ctree recursively perform univariate splits of the dependent variable based on values on a set of covariates. rpart and related algorithms usually employ information measures (such as the Gini coefficient) for selecting the cu... | Conditional inference trees vs traditional decision trees
For what it's worth:
both rpart and ctree recursively perform univariate splits of the dependent variable based on values on a set of covariates. rpart and related algorithms usually employ informatio |
1,771 | What are examples where a "naive bootstrap" fails? | If the quantity of interest, usually a functional of a distribution, is reasonably smooth and your data are i.i.d., you're usually in pretty safe territory. Of course, there are other circumstances when the bootstrap will work as well.
What it means for the bootstrap to "fail"
Broadly speaking, the purpose of the boots... | What are examples where a "naive bootstrap" fails? | If the quantity of interest, usually a functional of a distribution, is reasonably smooth and your data are i.i.d., you're usually in pretty safe territory. Of course, there are other circumstances wh | What are examples where a "naive bootstrap" fails?
If the quantity of interest, usually a functional of a distribution, is reasonably smooth and your data are i.i.d., you're usually in pretty safe territory. Of course, there are other circumstances when the bootstrap will work as well.
What it means for the bootstrap t... | What are examples where a "naive bootstrap" fails?
If the quantity of interest, usually a functional of a distribution, is reasonably smooth and your data are i.i.d., you're usually in pretty safe territory. Of course, there are other circumstances wh |
1,772 | What are examples where a "naive bootstrap" fails? | The following book has a chapter (Ch.9) devoted to "When Bootstrapping Fails Along with Remedies for Failures":
M. R. Chernick, Bootstrap methods: A guide for practitioners and researchers, 2nd ed. Hoboken N.J.: Wiley-Interscience, 2008.
The topics are:
Too Small of a Sample Size
Distributions with Infinite Moments
Es... | What are examples where a "naive bootstrap" fails? | The following book has a chapter (Ch.9) devoted to "When Bootstrapping Fails Along with Remedies for Failures":
M. R. Chernick, Bootstrap methods: A guide for practitioners and researchers, 2nd ed. Ho | What are examples where a "naive bootstrap" fails?
The following book has a chapter (Ch.9) devoted to "When Bootstrapping Fails Along with Remedies for Failures":
M. R. Chernick, Bootstrap methods: A guide for practitioners and researchers, 2nd ed. Hoboken N.J.: Wiley-Interscience, 2008.
The topics are:
Too Small of a... | What are examples where a "naive bootstrap" fails?
The following book has a chapter (Ch.9) devoted to "When Bootstrapping Fails Along with Remedies for Failures":
M. R. Chernick, Bootstrap methods: A guide for practitioners and researchers, 2nd ed. Ho |
1,773 | What are examples where a "naive bootstrap" fails? | The naive bootstrap depends on the sample size being large, so that the empirical CDF for the data are a good approximation to the "true" CDF. This ensures that sampling from the empirical CDF is very much like sampling from the "true" CDF. The extreme case is when you have only sampled one data point - bootstrapping... | What are examples where a "naive bootstrap" fails? | The naive bootstrap depends on the sample size being large, so that the empirical CDF for the data are a good approximation to the "true" CDF. This ensures that sampling from the empirical CDF is ver | What are examples where a "naive bootstrap" fails?
The naive bootstrap depends on the sample size being large, so that the empirical CDF for the data are a good approximation to the "true" CDF. This ensures that sampling from the empirical CDF is very much like sampling from the "true" CDF. The extreme case is when y... | What are examples where a "naive bootstrap" fails?
The naive bootstrap depends on the sample size being large, so that the empirical CDF for the data are a good approximation to the "true" CDF. This ensures that sampling from the empirical CDF is ver |
1,774 | How to annoy a statistical referee? | What particularly irritates me personally is people who clearly used user-written packages for statistical software but don't cite them properly, or at all, thereby failing to give any credit to the authors. Doing so is particularly important when the authors are in academia and their jobs depend on publishing papers t... | How to annoy a statistical referee? | What particularly irritates me personally is people who clearly used user-written packages for statistical software but don't cite them properly, or at all, thereby failing to give any credit to the a | How to annoy a statistical referee?
What particularly irritates me personally is people who clearly used user-written packages for statistical software but don't cite them properly, or at all, thereby failing to give any credit to the authors. Doing so is particularly important when the authors are in academia and thei... | How to annoy a statistical referee?
What particularly irritates me personally is people who clearly used user-written packages for statistical software but don't cite them properly, or at all, thereby failing to give any credit to the a |
1,775 | How to annoy a statistical referee? | Goodness me, so many things come to mind...
Stepwise regression
Splitting continuous data into
groups
Giving p-values but no measure of
effect size
Describing data using the mean and
the standard deviation without
indicating whether the data were
more-or-less symmetric and unimodal
Figures without clear captions (are
... | How to annoy a statistical referee? | Goodness me, so many things come to mind...
Stepwise regression
Splitting continuous data into
groups
Giving p-values but no measure of
effect size
Describing data using the mean and
the standard dev | How to annoy a statistical referee?
Goodness me, so many things come to mind...
Stepwise regression
Splitting continuous data into
groups
Giving p-values but no measure of
effect size
Describing data using the mean and
the standard deviation without
indicating whether the data were
more-or-less symmetric and unimodal
... | How to annoy a statistical referee?
Goodness me, so many things come to mind...
Stepwise regression
Splitting continuous data into
groups
Giving p-values but no measure of
effect size
Describing data using the mean and
the standard dev |
1,776 | How to annoy a statistical referee? | Irene Stratton and colleague published a short paper about a closely related question:
Stratton IM, Neil A. How to ensure your paper is rejected by the statistical reviewer. Diabetic Medicine 2005; 22(4):371-373. | How to annoy a statistical referee? | Irene Stratton and colleague published a short paper about a closely related question:
Stratton IM, Neil A. How to ensure your paper is rejected by the statistical reviewer. Diabetic Medicine 2005; 22 | How to annoy a statistical referee?
Irene Stratton and colleague published a short paper about a closely related question:
Stratton IM, Neil A. How to ensure your paper is rejected by the statistical reviewer. Diabetic Medicine 2005; 22(4):371-373. | How to annoy a statistical referee?
Irene Stratton and colleague published a short paper about a closely related question:
Stratton IM, Neil A. How to ensure your paper is rejected by the statistical reviewer. Diabetic Medicine 2005; 22 |
1,777 | How to annoy a statistical referee? | The code used to generate the simulated results is not provided. After asking for the code, it demands additional work to get it to run on a referee generated dataset. | How to annoy a statistical referee? | The code used to generate the simulated results is not provided. After asking for the code, it demands additional work to get it to run on a referee generated dataset. | How to annoy a statistical referee?
The code used to generate the simulated results is not provided. After asking for the code, it demands additional work to get it to run on a referee generated dataset. | How to annoy a statistical referee?
The code used to generate the simulated results is not provided. After asking for the code, it demands additional work to get it to run on a referee generated dataset. |
1,778 | How to annoy a statistical referee? | Plagiarism (theoretical or methodological). My first review was indeed for a paper figuring many unreferenced copy/paste from a well-established methodological paper published 10 years ago.
Just found a couple of interesting papers on this topic: Authorship and plagiarism in science.
In the same vein, I find falsificat... | How to annoy a statistical referee? | Plagiarism (theoretical or methodological). My first review was indeed for a paper figuring many unreferenced copy/paste from a well-established methodological paper published 10 years ago.
Just found | How to annoy a statistical referee?
Plagiarism (theoretical or methodological). My first review was indeed for a paper figuring many unreferenced copy/paste from a well-established methodological paper published 10 years ago.
Just found a couple of interesting papers on this topic: Authorship and plagiarism in science.... | How to annoy a statistical referee?
Plagiarism (theoretical or methodological). My first review was indeed for a paper figuring many unreferenced copy/paste from a well-established methodological paper published 10 years ago.
Just found |
1,779 | How to annoy a statistical referee? | When we ask the authors for
minor comment about an idea we have (in this sense, this not considered as a reason for rejecting the paper but just to be sure the authors are able to discuss another POV), or
unclear or contradicting results,
and that authors don't really answer in case (1) or that the incriminated res... | How to annoy a statistical referee? | When we ask the authors for
minor comment about an idea we have (in this sense, this not considered as a reason for rejecting the paper but just to be sure the authors are able to discuss another PO | How to annoy a statistical referee?
When we ask the authors for
minor comment about an idea we have (in this sense, this not considered as a reason for rejecting the paper but just to be sure the authors are able to discuss another POV), or
unclear or contradicting results,
and that authors don't really answer in c... | How to annoy a statistical referee?
When we ask the authors for
minor comment about an idea we have (in this sense, this not considered as a reason for rejecting the paper but just to be sure the authors are able to discuss another PO |
1,780 | How to annoy a statistical referee? | Confusing p-values and effect size (i.e. stating my effect is large because I have a really tiny p-value).
Slightly different than Stephan's answer of excluding effect sizes but giving p-values. I agree you should give both (and hopefully understand the difference!) | How to annoy a statistical referee? | Confusing p-values and effect size (i.e. stating my effect is large because I have a really tiny p-value).
Slightly different than Stephan's answer of excluding effect sizes but giving p-values. I agr | How to annoy a statistical referee?
Confusing p-values and effect size (i.e. stating my effect is large because I have a really tiny p-value).
Slightly different than Stephan's answer of excluding effect sizes but giving p-values. I agree you should give both (and hopefully understand the difference!) | How to annoy a statistical referee?
Confusing p-values and effect size (i.e. stating my effect is large because I have a really tiny p-value).
Slightly different than Stephan's answer of excluding effect sizes but giving p-values. I agr |
1,781 | How to annoy a statistical referee? | Not including effect sizes.
P-ing all over the research (I have to credit my favorite grad school professor for that line).
Giving a preposterous number of digits (males gained 3.102019 pounds more than females)
Not including page numbers (that makes it harder to review)
Misnumbering figures and tables
(as already ment... | How to annoy a statistical referee? | Not including effect sizes.
P-ing all over the research (I have to credit my favorite grad school professor for that line).
Giving a preposterous number of digits (males gained 3.102019 pounds more th | How to annoy a statistical referee?
Not including effect sizes.
P-ing all over the research (I have to credit my favorite grad school professor for that line).
Giving a preposterous number of digits (males gained 3.102019 pounds more than females)
Not including page numbers (that makes it harder to review)
Misnumbering... | How to annoy a statistical referee?
Not including effect sizes.
P-ing all over the research (I have to credit my favorite grad school professor for that line).
Giving a preposterous number of digits (males gained 3.102019 pounds more th |
1,782 | How to annoy a statistical referee? | When they don't sufficiently explain their analysis and/or include simple errors that make it difficult to work out what actually was done. This often includes throwing around a lot of jargon, by way of explanation, which is more ambiguous than the author seems to realize and also may be misused. | How to annoy a statistical referee? | When they don't sufficiently explain their analysis and/or include simple errors that make it difficult to work out what actually was done. This often includes throwing around a lot of jargon, by way | How to annoy a statistical referee?
When they don't sufficiently explain their analysis and/or include simple errors that make it difficult to work out what actually was done. This often includes throwing around a lot of jargon, by way of explanation, which is more ambiguous than the author seems to realize and also ma... | How to annoy a statistical referee?
When they don't sufficiently explain their analysis and/or include simple errors that make it difficult to work out what actually was done. This often includes throwing around a lot of jargon, by way |
1,783 | How to annoy a statistical referee? | Using causal language to describe associations in observational data when omitted variables are almost certainly a serious concern. | How to annoy a statistical referee? | Using causal language to describe associations in observational data when omitted variables are almost certainly a serious concern. | How to annoy a statistical referee?
Using causal language to describe associations in observational data when omitted variables are almost certainly a serious concern. | How to annoy a statistical referee?
Using causal language to describe associations in observational data when omitted variables are almost certainly a serious concern. |
1,784 | How to annoy a statistical referee? | Coming up with new words for the existing concepts, or, vice versa, using the existing terms to denote something different.
Some of the existing terminology differentials has long settled in the literature: longitudinal data in biostatistics vs. panel data in econometrics; cause and effect indicators in sociology vs. ... | How to annoy a statistical referee? | Coming up with new words for the existing concepts, or, vice versa, using the existing terms to denote something different.
Some of the existing terminology differentials has long settled in the lite | How to annoy a statistical referee?
Coming up with new words for the existing concepts, or, vice versa, using the existing terms to denote something different.
Some of the existing terminology differentials has long settled in the literature: longitudinal data in biostatistics vs. panel data in econometrics; cause and... | How to annoy a statistical referee?
Coming up with new words for the existing concepts, or, vice versa, using the existing terms to denote something different.
Some of the existing terminology differentials has long settled in the lite |
1,785 | How to annoy a statistical referee? | When authors use the one statistical test they know (in my field, usually a t-test or an ANOVA), ad infinitum, regardless of whether it's appropriate. I recently reviewed a paper where the authors wanted to compare a dozen different treatment groups, so they had done a two-sample t-test for every possible pair of trea... | How to annoy a statistical referee? | When authors use the one statistical test they know (in my field, usually a t-test or an ANOVA), ad infinitum, regardless of whether it's appropriate. I recently reviewed a paper where the authors wa | How to annoy a statistical referee?
When authors use the one statistical test they know (in my field, usually a t-test or an ANOVA), ad infinitum, regardless of whether it's appropriate. I recently reviewed a paper where the authors wanted to compare a dozen different treatment groups, so they had done a two-sample t-... | How to annoy a statistical referee?
When authors use the one statistical test they know (in my field, usually a t-test or an ANOVA), ad infinitum, regardless of whether it's appropriate. I recently reviewed a paper where the authors wa |
1,786 | How to annoy a statistical referee? | Zero consideration of missing data.
Many practical applications use data for which there are at least some missing values. This is certainly very true in epidemiology. Missing data presents problems for many statistical methods - including linear models. Missing data with linear models is often dealt with through dele... | How to annoy a statistical referee? | Zero consideration of missing data.
Many practical applications use data for which there are at least some missing values. This is certainly very true in epidemiology. Missing data presents problems | How to annoy a statistical referee?
Zero consideration of missing data.
Many practical applications use data for which there are at least some missing values. This is certainly very true in epidemiology. Missing data presents problems for many statistical methods - including linear models. Missing data with linear mod... | How to annoy a statistical referee?
Zero consideration of missing data.
Many practical applications use data for which there are at least some missing values. This is certainly very true in epidemiology. Missing data presents problems |
1,787 | How to annoy a statistical referee? | Reporting effects that "approached significance ( p < .10 for example) and then writing about them as though they had attained significance at a more stringent and acceptable level.
Running multiple Structural Equation Models that were not nested and then writing about them as though they were nested.
Taking a well-est... | How to annoy a statistical referee? | Reporting effects that "approached significance ( p < .10 for example) and then writing about them as though they had attained significance at a more stringent and acceptable level.
Running multiple S | How to annoy a statistical referee?
Reporting effects that "approached significance ( p < .10 for example) and then writing about them as though they had attained significance at a more stringent and acceptable level.
Running multiple Structural Equation Models that were not nested and then writing about them as though... | How to annoy a statistical referee?
Reporting effects that "approached significance ( p < .10 for example) and then writing about them as though they had attained significance at a more stringent and acceptable level.
Running multiple S |
1,788 | How to annoy a statistical referee? | I recommend the following two articles:
Martin Bland:
How to Upset the Statistical Referee
This is based on a series of talks given by Martin Bland, along with data from other statistical referees (‘a convenience sample with a low response rate’). It ends with an 11-point list of ‘[h]ow to avoid upsetting the statistic... | How to annoy a statistical referee? | I recommend the following two articles:
Martin Bland:
How to Upset the Statistical Referee
This is based on a series of talks given by Martin Bland, along with data from other statistical referees (‘a | How to annoy a statistical referee?
I recommend the following two articles:
Martin Bland:
How to Upset the Statistical Referee
This is based on a series of talks given by Martin Bland, along with data from other statistical referees (‘a convenience sample with a low response rate’). It ends with an 11-point list of ‘[h... | How to annoy a statistical referee?
I recommend the following two articles:
Martin Bland:
How to Upset the Statistical Referee
This is based on a series of talks given by Martin Bland, along with data from other statistical referees (‘a |
1,789 | How to annoy a statistical referee? | I'm most (and most frequently) annoyed by "validation" aiming at generalization error of predictive models where the test data is not independent (e.g. typically multiple measurements per patient in the data, out-of-bootstrap or cross validation splitting measurements not patients).
Even more annoying, papers that give... | How to annoy a statistical referee? | I'm most (and most frequently) annoyed by "validation" aiming at generalization error of predictive models where the test data is not independent (e.g. typically multiple measurements per patient in t | How to annoy a statistical referee?
I'm most (and most frequently) annoyed by "validation" aiming at generalization error of predictive models where the test data is not independent (e.g. typically multiple measurements per patient in the data, out-of-bootstrap or cross validation splitting measurements not patients).
... | How to annoy a statistical referee?
I'm most (and most frequently) annoyed by "validation" aiming at generalization error of predictive models where the test data is not independent (e.g. typically multiple measurements per patient in t |
1,790 | How to annoy a statistical referee? | Using Microsoft Word rather than LaTeX. | How to annoy a statistical referee? | Using Microsoft Word rather than LaTeX. | How to annoy a statistical referee?
Using Microsoft Word rather than LaTeX. | How to annoy a statistical referee?
Using Microsoft Word rather than LaTeX. |
1,791 | How to annoy a statistical referee? | Using "data" in a singular sense. Data ARE, they never is. | How to annoy a statistical referee? | Using "data" in a singular sense. Data ARE, they never is. | How to annoy a statistical referee?
Using "data" in a singular sense. Data ARE, they never is. | How to annoy a statistical referee?
Using "data" in a singular sense. Data ARE, they never is. |
1,792 | How to annoy a statistical referee? | For me by far is , attributing cause without any proper causal analysis or when there is improper causal inference.
I also hate it when zero attention is given to how missing data was handled. I see so many papers too where the authors simply perform complete case analysis and make no mention of whether or not the re... | How to annoy a statistical referee? | For me by far is , attributing cause without any proper causal analysis or when there is improper causal inference.
I also hate it when zero attention is given to how missing data was handled. I see | How to annoy a statistical referee?
For me by far is , attributing cause without any proper causal analysis or when there is improper causal inference.
I also hate it when zero attention is given to how missing data was handled. I see so many papers too where the authors simply perform complete case analysis and make... | How to annoy a statistical referee?
For me by far is , attributing cause without any proper causal analysis or when there is improper causal inference.
I also hate it when zero attention is given to how missing data was handled. I see |
1,793 | Relationship between poisson and exponential distribution | I will use the following notation to be as consistent as possible with the wiki (in case you want to go back and forth between my answer and the wiki definitions for the poisson and exponential.)
$N_t$: the number of arrivals during time period $t$
$X_t$: the time it takes for one additional arrival to arrive assuming ... | Relationship between poisson and exponential distribution | I will use the following notation to be as consistent as possible with the wiki (in case you want to go back and forth between my answer and the wiki definitions for the poisson and exponential.)
$N_t | Relationship between poisson and exponential distribution
I will use the following notation to be as consistent as possible with the wiki (in case you want to go back and forth between my answer and the wiki definitions for the poisson and exponential.)
$N_t$: the number of arrivals during time period $t$
$X_t$: the ti... | Relationship between poisson and exponential distribution
I will use the following notation to be as consistent as possible with the wiki (in case you want to go back and forth between my answer and the wiki definitions for the poisson and exponential.)
$N_t |
1,794 | Relationship between poisson and exponential distribution | For a Poisson process, hits occur at random independent of the past, but with a known long term average rate $\lambda$ of hits per time unit. The Poisson distribution would let us find the probability of getting some particular number of hits.
Now, instead of looking at the number of hits, we look at the random variabl... | Relationship between poisson and exponential distribution | For a Poisson process, hits occur at random independent of the past, but with a known long term average rate $\lambda$ of hits per time unit. The Poisson distribution would let us find the probability | Relationship between poisson and exponential distribution
For a Poisson process, hits occur at random independent of the past, but with a known long term average rate $\lambda$ of hits per time unit. The Poisson distribution would let us find the probability of getting some particular number of hits.
Now, instead of lo... | Relationship between poisson and exponential distribution
For a Poisson process, hits occur at random independent of the past, but with a known long term average rate $\lambda$ of hits per time unit. The Poisson distribution would let us find the probability |
1,795 | Relationship between poisson and exponential distribution | The other answers do a good job of explaining the math. I think it helps to consider a physical example. When I think about a Poisson process, I always come back to the idea of cars passing on a road. Lambda is the average number of cars that pass per unit of time, let's say 60/hour (lambda = 60). We know, however, tha... | Relationship between poisson and exponential distribution | The other answers do a good job of explaining the math. I think it helps to consider a physical example. When I think about a Poisson process, I always come back to the idea of cars passing on a road. | Relationship between poisson and exponential distribution
The other answers do a good job of explaining the math. I think it helps to consider a physical example. When I think about a Poisson process, I always come back to the idea of cars passing on a road. Lambda is the average number of cars that pass per unit of ti... | Relationship between poisson and exponential distribution
The other answers do a good job of explaining the math. I think it helps to consider a physical example. When I think about a Poisson process, I always come back to the idea of cars passing on a road. |
1,796 | Relationship between poisson and exponential distribution | The Poisson Distribution is normally derived from the Binomial Distribution (both discrete). This you'll find on Wiki.
However, the Poisson distribution (discrete) can also be derived from the Exponential Distribution (continuous).
I've added the proof to Wiki (link below):
https://en.wikipedia.org/wiki/Talk:Poisson_di... | Relationship between poisson and exponential distribution | The Poisson Distribution is normally derived from the Binomial Distribution (both discrete). This you'll find on Wiki.
However, the Poisson distribution (discrete) can also be derived from the Exponen | Relationship between poisson and exponential distribution
The Poisson Distribution is normally derived from the Binomial Distribution (both discrete). This you'll find on Wiki.
However, the Poisson distribution (discrete) can also be derived from the Exponential Distribution (continuous).
I've added the proof to Wiki (... | Relationship between poisson and exponential distribution
The Poisson Distribution is normally derived from the Binomial Distribution (both discrete). This you'll find on Wiki.
However, the Poisson distribution (discrete) can also be derived from the Exponen |
1,797 | Relationship between poisson and exponential distribution | While the other answers here go into more explanatory detail, I am going to give you a simple summary of the equation relating a set of IID exponential random variables and a generated Poisson random variable. A Poisson random variable with parameter $\lambda > 0$ can be generated by counting the number of sequential ... | Relationship between poisson and exponential distribution | While the other answers here go into more explanatory detail, I am going to give you a simple summary of the equation relating a set of IID exponential random variables and a generated Poisson random | Relationship between poisson and exponential distribution
While the other answers here go into more explanatory detail, I am going to give you a simple summary of the equation relating a set of IID exponential random variables and a generated Poisson random variable. A Poisson random variable with parameter $\lambda >... | Relationship between poisson and exponential distribution
While the other answers here go into more explanatory detail, I am going to give you a simple summary of the equation relating a set of IID exponential random variables and a generated Poisson random |
1,798 | Generate a random variable with a defined correlation to an existing variable(s) | Here's another one: for vectors with mean 0, their correlation equals the cosine of their angle. So one way to find a vector $x$ with exactly the desired correlation $r$, corresponding to an angle $\theta$:
get fixed vector $x_1$ and a random vector $x_2$
center both vectors (mean 0), giving vectors $\dot{x}_{1}$, $\... | Generate a random variable with a defined correlation to an existing variable(s) | Here's another one: for vectors with mean 0, their correlation equals the cosine of their angle. So one way to find a vector $x$ with exactly the desired correlation $r$, corresponding to an angle $\t | Generate a random variable with a defined correlation to an existing variable(s)
Here's another one: for vectors with mean 0, their correlation equals the cosine of their angle. So one way to find a vector $x$ with exactly the desired correlation $r$, corresponding to an angle $\theta$:
get fixed vector $x_1$ and a ra... | Generate a random variable with a defined correlation to an existing variable(s)
Here's another one: for vectors with mean 0, their correlation equals the cosine of their angle. So one way to find a vector $x$ with exactly the desired correlation $r$, corresponding to an angle $\t |
1,799 | Generate a random variable with a defined correlation to an existing variable(s) | I will describe the most general possible solution. Solving the problem in this generality allows us to achieve a remarkably compact software implementation: just two short lines of R code suffice. At the end is a generalization to multiple $Y$ vectors, with working code.
Pick a vector $X$, of the same length as $Y$, ... | Generate a random variable with a defined correlation to an existing variable(s) | I will describe the most general possible solution. Solving the problem in this generality allows us to achieve a remarkably compact software implementation: just two short lines of R code suffice. A | Generate a random variable with a defined correlation to an existing variable(s)
I will describe the most general possible solution. Solving the problem in this generality allows us to achieve a remarkably compact software implementation: just two short lines of R code suffice. At the end is a generalization to multip... | Generate a random variable with a defined correlation to an existing variable(s)
I will describe the most general possible solution. Solving the problem in this generality allows us to achieve a remarkably compact software implementation: just two short lines of R code suffice. A |
1,800 | Generate a random variable with a defined correlation to an existing variable(s) | Here's another computational approach (the solution is adapted from a forum post by Enrico Schumann).
According to Wolfgang (see comments), this is computationally identical to the solution proposed by ttnphns.
In contrast to caracal's solution it does not produce a sample with the exact correlation of $\rho$, but two... | Generate a random variable with a defined correlation to an existing variable(s) | Here's another computational approach (the solution is adapted from a forum post by Enrico Schumann).
According to Wolfgang (see comments), this is computationally identical to the solution proposed b | Generate a random variable with a defined correlation to an existing variable(s)
Here's another computational approach (the solution is adapted from a forum post by Enrico Schumann).
According to Wolfgang (see comments), this is computationally identical to the solution proposed by ttnphns.
In contrast to caracal's so... | Generate a random variable with a defined correlation to an existing variable(s)
Here's another computational approach (the solution is adapted from a forum post by Enrico Schumann).
According to Wolfgang (see comments), this is computationally identical to the solution proposed b |
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