# Questions tagged [regression]

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

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### Regression with sigmoid link for diminishing returns?

I am working with some researchers who would like to see how a dozen or so "life inputs" can affect a measure of happiness. My feeling is that treating these as additive, as in a regular ...
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### Insignificant likelihood ratio but significant parameter estimate?

So I am running multinomial logistic regression and the likelihood ratio (chi aquare) is insignificant, but the predictor is significant in the parameter estimates. I don't know if I should interpret ...
1answer
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### ANOVA 1-way - How to calculate SSF

I'm studying and I can't find an answer to make the ANOVA table for this. I have 4 groups, with mean and variance group. My N=60, n=15 for each group. To find Sum Square Errors I did Sum( ni * ...
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### Weighting logistic regression in R

I'm doing a simple logistic regression in R where I'm trying predict the outcome of sales calls. I have data/observations from the past 12 months but I would want that the most recent observations ...
0answers
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### Adjusted R squared formula [duplicate]

I am studying linear regression lately and I notice this adjusted r-squared formula in a youtube video: $$adj. R^2 = \frac{\frac{SSE}{n-k}}{\frac{SSTO}{n-1}}$$ While the formula that I know is this: ...
0answers
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### What arguments can be used for/against clustering standard errors and/or estimating coefficients using the fixed effects model?

I'm writing a thesis based by using the following papers as a starting point: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3051151 https://www.sciencedirect.com/science/article/abs/pii/...
1answer
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### I am doing a simple Moderation regression: there is multicolinearity in my model when I add the interaction term. I want to check if this is ok

The first step of my regressionmodel with both predictor variables and the outcome variable meet all assumptions. However when I add the second term there is multicolinearity. This seems obvious since ...
0answers
30 views

### Kernel trick to logistic regression

Why can't I apply the kernel trick in logistic regression? My reasoning is: in SVM the logit is: $z = \sum_i \alpha_i K(x_i, x) + b$ Where K is the kernel function. In logistic regression you have ...
0answers
41 views

### $H_0$ vs $H_1$ in diagnostic testing

Consider diagnostic testing of a fitted model, e.g. testing whether regression residuals are autocorrelated (a violation of an assumption) or not (no violation). I have a feeling that the null ...
0answers
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### How can I predict a new x using coefficient of B-spline basis functions?

I have a nonparametric regression problem using B-spline basis functions. The range of x is a vector as (350,370,390,410,430). I`ve obtained coefficients. How can I predict the value of response for x=...
1answer
41 views

### Change in mean of y given change in mean of x?

I am a bit confused about interpreting simple linear regression coefficients. My understanding is that for a linear regression equation, increasing x by one unit corresponds to a change in the mean of ...
2answers
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1answer
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### How to interpret the coefficient when both Y and X are expressed in %?

I have an issue: my dependent variable is expressed in % (it's the % of people that think they will lose their job) , my regressor is expressed in % (it's the share of foreign residents on total ...
1answer
41 views

### How to build a saturated model

I am struggling with the understanding of a saturated model. As far as I know, the saturated model is the model that have as many parameter as the data points. But I don't know how to build it or what ...
1answer
33 views

### Regression for Completely Randomized Experiments

I'm reading Guido W. Imbens and Donald B. Rubin's book on Causal Inference. In chapter 7 they try to justify using a regression model to estimate the mean casual effect in a completely random ...
1answer
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### Fixed effects in DAGs

Let's imagine I'm interested in studying the causal effect of beliefs in some ideas and behavior related to these ideas (say, if I believe sunscreen is good for my health, I use more sunscreen etc.). ...
1answer
21 views

### Robust linear regression for group differences

I am trying to understand what it means when a p value for a grouping variable is < 0.05 after running robust linear regression. Does this mean that the 2 groups significantly differ with respect ...
2answers
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### calculate adjusted p and t value after Bonferroni Correction

I am using linear mixed effect models to analyse my data. I am looking at how 4 independent variables might affect 5 dependent variables. I read an article by von der Malsburg and Angele (2017) which ...