# Tagged Questions

This tag is a signal that the question focuses on a problem particular to multivariate analysis, such as multiple correlations or interactions.

10k views

### How can a regression be significant yet all predictors be non-significant?

My multiple regression analysis model has a statistically significant F value however all beta values are statistically non-significant. All the regression assumptions are met. No multicollinearity ...
17k views

### When should you center your data & when should you standardize?

In some literature, I have read that a regression with multiple explanatory variables, if in different units, needed to be standardized. (Standardizing consists in subtracting the mean and dividing ...
2k views

### How to choose between ANOVA and ANCOVA in a designed experiment?

I am conducting an experiment which has the following: DV: Slice consumption (continuous or could be categorical) IV: Healthy message, unhealthy message, no message (control) (3 groups in which ...
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### Significance of coefficients in linear regression: significant t-test vs non-significant F-statistic [duplicate]

I'm fitting a multiple linear regression model between 4 categorical variables (with 4 levels each) and a numerical output. My dataset has 43 observations. R gives me the following p-values from the ...
2k views

### How can adding a 2nd IV make the 1st IV significant?

I have what is probably a simple question, but it is baffling me right now, so I am hoping you can help me out. I have a least squares regression model, with one independent variable and one ...
2k views

### Not-significant F but a significant coefficient in multiple linear regression

I have a regression with two continuous predictors and one dichotomous predictor in Model 1 and two interactions of each of the continuous predictors with the dichotomous predictor in Model 2. The ...
486 views

### Why ANOVA/Regression results change when controlling for another variable

This question might be very basic, but somehow I don't understand this point. Suppose initially I used a univariate regression equation such as GDP=a+b*Income ...
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### How to interpret inconsistent Beta values in different steps of hierarchical regression analysis?

I did hierarchical regression analysis on my data due to having moderation effects in my research model. R2 increased from .695 in model1 (main effect only) to .734 in model2 (main &interaction ...
549 views

### In what order should you do linear regression diagnostics?

In linear regression analysis, we analyze outliers, investigate multicollinearity, test heteroscedasticty. The question is: Is there any order to apply these? I mean, do we have to analyze outliers ...
230 views

### What is an unbiased estimate of population R-square?

I am interested in getting an unbiased estimate of $R^2$ in a multiple linear regression. On reflection, I can think of two different values that an unbiased estimate of $R^2$ might be trying to ...
7k views

### Is adjusting p-values in a multiple regression for multiple comparisons a good idea?

Lets assume you are a social science researcher/econometrician trying to find relevant predictors of demand for a service. You have 2 outcome/dependent variables describing the demand (using the ...
1k views

### Transforming variables for multiple regression in R

I am trying to perform a multiple regression in R. However, my dependent variable has the following plot: Here is a scatterplot matrix with all my variables ...
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### VIF, condition Index and eigenvalues

I am currently assessing multicollinearity in my datasets. What threshold values of VIF and condition index below/above suggest a problem? VIF: I have heard that VIF $\geq 10$ is a problem. After ...
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### How can I use the value of $R^2$ to test the linearity assumption in multiple regression analysis?

The below graphs are residual scatter plots of a regression test for which "normality", "homoscedasticity" and "independence" assumptions have already been met for sure! For testing the "linearity" ...
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### Multivariate multiple regression in R

I have 2 dependent variables (DVs) each of whose score may be influenced by the set of 7 independent variables (IVs). DVs are continuous, while the set of IVs consists of a mix of continuous and ...
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### How to deal with multicollinearity when performing variable selection?

I have a dataset with 9 continuous independent variables. I'm trying to select amongst these variables to fit a model to a single percentage (dependent) variable, ...
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### Either quadratic or interaction term is significant in isolation, but neither are together

As part of an assignment, I had to fit a model with two predictor variables. I then had to draw a plot of the models' residuals against one of the included predictors and make changes based on that. ...
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### Why are rlm() regression coefficient estimates different than lm() in R?

I am using rlm in the R MASS package to regress a multivariate linear model. It works well for a number of samples but I am getting quasi-null coefficients for a particular model: ...
2k views

### How to do a generalized linear model with multiple dependent variables in R?

I have six dependent variables (count data) and several independent variables, I see that in a MMR the script goes like this: ...
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### How can you have a non-significant multiple regression model w/ significant predictors? [duplicate]

Possible Duplicate: Not-significant F but a significant coefficient in multiple linear regression How can a regression be significant yet all predictors be non-significant? Significance of ...
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### Where is the shared variance between all IVs in a linear multiple regression equation?

In a linear multiple regression equation, if the beta weights reflect the contribution of each individual independent variable over and above the contribution of all the other IVs, where in the ...
2k views

### Multicollinearity when individual regressions are significant, but VIFs are low

I have 6 variables ($x_{1}...x_{6}$) that I am using to predict $y$. When performing my data analysis, I first tried a multiple linear regression. From this, only two variables were significant. ...
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### Should I run separate regressions for every community, or can community simply be a controlling variable in an aggregated model?

I am running an OLS model with a continuous asset index variable as the DV. My data is aggregated from three similar communities in close geographic proximity to one another. Despite this, I thought ...
255 views

### Help me fit this non-linear multiple regression that has defied all previous efforts

EDIT: Since making this post, I have followed up with an additional post here. Summary of the text below: I am working on a model and have tried linear regression, Box Cox transformations and GAM but ...
507 views

### How to model this odd-shaped distribution (almost a reverse-J)

My dependent variable shown below doesn't fit any stock distribution that I know of. Linear regression produces somewhat non-normal, right-skewed residuals that relate to predicted Y in an odd way ...
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### Fitting a zero-inflated negative binomial regression with R

In this thread, I laid out a problem involving fitting a model that attempts to use minor league baseball statistics to predict success at the major league level (explained in full in the thread). ...
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### Is it possible to have a variable significant in multiple regression but not significant in stepwise regression?

I have run a stepwise regression and found that some of the selected variables are not significant yet in a multiple regression with all variables included in the model those variables were ...
9k views

### What is the correct way to test for significant differences between coefficients?

I'm hoping someone can help straighten out a point of confusion for me. Say I want to test whether 2 sets of regression coefficients are significantly different from each other, with the following set ...
892 views

### Parallel lines on residual vs fitted plot

I have a multiple regression problem, which I tried to solve using simple multiple regression: model1 <- lm(Y ~ X1 + X2 + X3 + X4 + X5, data=data) This seems ...
326 views

### Improvement of regression model

I am just learning R. I have developed a regression model with six predictor variables. While developing it, I found the relationships are not very linear. So, maybe because of this the predictions of ...
1k views

### Factor analysis and regression

I have a question about how to interpret a regression analysis I did following a factor analysis. I did principal axis factoring (direct oblimin) I got a 3 factor solution. The three factors were ...
5k views

### How to present results of a Lasso using glmnet?

I would like to find predictors for a continuous dependent variable out of a set of 30 independent variables. I am using Lasso regression as implemented in the glmnet package in R. Here is some dummy ...
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### Is there a difference between 'controlling for' and 'ignoring' other variables in multiple regression?

The coefficient of an explanatory variable in a multiple regression tells us the relationship of that explanatory variable with the dependent variable. All this, while 'controlling' for the other ...
331 views

### Suppression effect in regression: definition and visual explanation/depiction

What is a suppressor variable in multiple regression and what might be the ways to display suppression effect visually (its mechanics or its evidence in results)? I'd like to invite everybody who has ...
12k views

### Acceptable r-square value for multiple linear regression model

I'm currently working on my thesis, more specifically I'm analyzing some data collected from researchers about the project's they're working on. In the end, I have performed a multiple linear ...
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### When should one use multiple regression with dummy coding vs. ANCOVA?

I recently analyzed an experiment that manipulated 2 categorical variables and one continuous variable using ANCOVA. However, a reviewer suggested that multiple regression with the categorical ...
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### Multiple imputation and model selection

Multiple imputation is fairly straightforward when you have an a priori linear model that you want to estimate. However, things seem to be a bit trickier when you actually want to do some model ...
491 views

### Sandwich estimator intuition

Wikipedia and the R sandwich package vignette give good information about the assumptions supporting OLS coefficient standard errors and the mathematical background of the sandwich estimators. I'm ...
120 views

### What do we call multiple testing?

I can think of different “types” of multiple testing when using linear models for example: Multiple inferences because we have several dependent variables Multiple inferences because we have several ...
249 views

### Possible range of $R^2$

Suppose are three time series, $X_1$, $X_2$ and $Y$ Running ordinary linear regression on $Y$ ~ $X_1$ ($Y = b X_1 + b_0 + \epsilon$ ), we get $R^2 = U$. The ordinary linear regression $Y$ ~ $X_2$ get ...
2k views

### Positive correlation and negative regressor coefficient sign

Is it possible to obtain a positive correlation between a regressor and a response (+0,43) and, after that, obtain a negative coefficient in the fitted regression ...
368 views

### Dealing with a categorical variable that can take multiple levels simultaneously

I recently posted a question with many parts and I'd like to focus in on just one issue that I didn't emphasize in the original post. My data is a list of records, each one representing an ...
246 views

### Regression $R^2$ and correlations

I understand that for simple linear regression, the sample correlation coefficient is the square root of the $R^2$. But that's just for a simple (i.e., single variable) regression ...
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### What happens when I include a squared variable in my regression?

I start with my OLS regression: $$y = \beta _0 + \beta_1x_1+\beta_2 D + \varepsilon$$ where D is a dummy variable, the estimates become different from zero with a low p-value. I then preform a ...
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### At what VIF level should you switch from OLS to ridge-regression?

Regarding multicollinearity, is it recommended to use ridge-regression if you have some covariates with VIF values around 10 in the OLS model? What would be the best VIF level to use to decide ...
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### Minimum number of observations for multiple linear regression

I am doing multiple linear regression. I have 21 observations and 5 variables. My aim is just finding the relation between variables Is my data set enough to do multiple regression? The t-test ...
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### Multiple linear regression for hypothesis testing

I am familiar with using multiple linear regressions to create models of various variables. However, I was curious if regression tests are ever used to do any sort of basic hypothesis testing. If so, ...
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### How to model multivariate time series

I have a set of $n=1000$ samples of 4 dimensions (multivariate) where each measurement obtained from GPS tracking data is taken at a time interval representing spatial coordinates $(x,y)$, velocity. ...