# Questions tagged [regression]

Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

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### Difference between bias-variance dilemma and overfitting

I'm wondering what difference it makes whether we talk about bias-variance dilemma where fitting a regression line to the given dataset reduces bias and increases variance or whether we talk about ...
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### Interpretation of incidence-rate ratios

So, I want to fit a random effects negative-binomial model. For such a model STATA can produce exponentiated coefficients. According to the help file such coefficients can be interpreted as incidence-...
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### When can I suppress the intercept using treatreg?

Can I suppress the intercept if I know the treatment will be zero if the independent variables are zero. Also, can I suppress the intercept if I know the right hand side of the primary regression ...
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### How to specify Bayesian mixed effects model in BUGS

I posted this earlier in the week then retracted the question when I found a good source, not wanting to waste people's time. I haven't made much progress I'm afraid. In trying to be a good citizen ...
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### Difference between t-test and ANOVA in linear regression

I wonder what differences are between t-test and ANOVA in linear regression? Is a t-test to test whether any one of the slopes and intercept has mean zero, while ANOVA to test whether all slopes have ...
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### How do I transform a data generation specification in R into a BUGS/JAGS specification

I am at a loss about what the BUGS/JAGS specification of the following should look like. The background is that one person has four measurements taken by four different instruments. Each instrument ...
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### Which link function for a regression when Y is continuous between 0 and 1?

I've always used logistic regression when Y was categorical data 0 or 1. Now I have this dependent variable that is really a ratio/probability. That means it can be any number between 0 and 1. I ...
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### Why would significance of F-value change in linear regression if you change the reference group?

For categorical predictors with k levels, it doesnt matter what you choose as your reference group. So why would the F-value significance change in the linear regression if you change the reference ...
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### What distributions are for the slope and for the intercept in linear regression?

In linear regression from a dataset $\{ (x_i, y_i), i=1,\cdots,N \}$, I wonder what distributions are for the slope and for the intercept? In Excel output, t-tests are used to tell whether the slope ...
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### Multiple linear regression on a data set with Python?

I'll preface my question with the fact that I'm just learning about linear regression so I may be thinking about this wrong. I have a set of data. In this set I have one dependent variable and about ...
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### How to interpret basic output from a regression analysis?

I have been trying to interpret the results below, but I am finding it difficult. I wonder if someone could help me. All answers highly appreciated. Number of obs = 30 F( 2, 27) = 19.73 Prob > ...
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### What is the effect of dichotomising variables?

When dichotomising variables, what information is lost in the process? How does a dichotomisation help in the analyses?
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### What type of regression analysis is best to model the relationship between self-efficacy and activity level over three time points?

I am conducting an orthopedic study to predict improvements in activity level (dependent variable) based on a type of self-efficacy scale (the independent variable). There are, however, other ...
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### How to perform repeated measures ordinal logistic regression using SPSS? [closed]

I'm usually a UseR. However, for didactic purposes I have to use SPSS today. I have to specify a General linear model with ordinal structure because what I'm examining is: Change in Likert scale ...
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### Determine gradient from past samples

Again this question may be simple for you, but it is an important aspect for my classification problem. Let`s say I have 5 attributes, which are: ...
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### Properties of logistic regressions

We're working with some logistic regressions and we have realized that the average estimated probability always equals the proportion of ones in the sample; that is, the average of fitted values ...
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### In linear regression, is the $R^2$ value enough to assess whether the relationship between the independent and dependent variable is linear?

In linear regression, is the $R^2$ value enough to assess whether the relationship between the independent and dependent variable is linear? It gives the amount of variability in the dependent ...
167k views

### Difference between confidence intervals and prediction intervals

For a prediction interval in linear regression you still use $\hat{E}[Y|x] = \hat{\beta_0}+\hat{\beta}_{1}x$ to generate the interval. You also use this to generate a confidence interval of $E[Y|x_0]$....
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### Minimum cluster size requirements? Minimum sample size requirements for clustered standard errors?

I have a sample with little over 100 observations and 50 clusters, one quarter of which have only one observation. Is it correct to calculate clustered standard errors in a linear regression that uses ...
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### Does including both raw and per capita measures as predictors reduce significance of either predictor?

I'm running a regression on independent variables, some of which are measured in different units, for example: The amount of broadband connections in a country The amount of broadband connections in ...
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### How to interpret the conditional expected value in a regression?

If you want to estimate $E[y| x = \text{some value}]$ is this just a matter of plugging $x$ into the regression equation? Because you estimate the regression coefficients, therefore the value you get ...
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Is "unadjusted" basically just simple linear regression whereas "adjusted" is multiple regression? For example, looking at the effect of x on y adjusting for other variables like a, b and c versus ...
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### What is a complete list of the usual assumptions for linear regression?

What are the usual assumptions for linear regression? Do they include: a linear relationship between the independent and dependent variable independent errors normal distribution of errors ...
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### Selecting regression model for a non-negative integer response

I have a series of non-negative integers $y=(y_1,y_2,..., y_n)$ and a design matrix $y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \beta_3 x_1 x_2$, where $x_0$ and $x_1$ are $0$ or $1$, $x_1x_2$ is the ...
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### How low multiple R-squared value is enough to reject a model?

I'm doing a simple linear regression I tried: ...
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### How is it possible that these variances are equal?

I'm using the Fligner-Killen test to analyze the residuals of a linear regression. I subdivide those residuals in three groups and then I do the FK test to check the homogeneity of variances. The ...
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### Which type of regression analysis should I use for consumption pattern data?

I conducted a survey asking people of various income groups about their annual expenditure under various consumption brackets such as food, clothing, housing etc. I then calculated the amount spent by ...
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### Local polynomial (linear) regression of binary data — logit transformation?

I got a bit confused about how to fit a local polynomial to binary outcomes if I would rather approximate the underlying index (within a link function) instead. (Basically for the same reason why ...
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### Is it reasonable to treat a three point Likert dependent variable as a continuous variable?

I have a three point Likert scale question: How happy are you? 1= low levels of happiness 2= medium levels 3= high levels I want to do multiple ...
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### Determining the influential features for an outcome

I have a small table like this ...
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### How to test if the errors of a linear regression are constant? [duplicate]

Possible Duplicate: Kruskal-Wallis or Fligner test to check homogeneity of variances? Is there a test to check if the residuls of a linear regression are constant? Thanks