Linked Questions

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1answer
2k views

Adding new variables makes regression coefficients individually insignificant [duplicate]

I have a multiple regression where all my coefficients are significant. When I add new variables my initial variables become insignificant. Furthermore, my new variables (that in a simple regression ...
0
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1answer
1k views

Sign change of a coefficient in logistic regression? [duplicate]

I am running a logistic regression with 5 continuous independent variables (IV). The problem is that IV4 when taken alone has a positive correlation with outcome (coeff > 0), and when taken with the ...
3
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0answers
639 views

Why signs of coefficients change when doing multivariate vs. univariate logit regression? [duplicate]

Excuse my dumb question, but I did an univariate logistic regression where the sign of the coefficient of my variable was negative (and it was significant). Once I have input it into a multivariate ...
1
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2answers
302 views

when some of your coefficients in multivariate logistic regression model is negative [duplicate]

when some of your coefficients in multivariate logistic regression model is negative while i know these variable have positive sign in univariate model, What should I do؟
0
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1answer
75 views

Binary Logit Regression: Binary independent variable's coefficient sign reversal with addition of more Regressors.s [duplicate]

I have signed a confidentiality agreement with my university so I have hidden some of the content in the picture, I hope that is okay with the community here. This is the output of R code with glm ...
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0answers
51 views

Getting the wrong sign [duplicate]

In a regression, when you get negative coefficient which you know should be positive, why it is necessary to include possible omitted variable that is likely to have positive coefficient and ...
0
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0answers
27 views

Logistic regression understanding variable nature [duplicate]

I have 2 categorically dependent variables(both binomial) in logistic regression which individually both give positive estimates against the response(binomial). However if modeled together one give ...
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0answers
20 views

Why anova has different result on the same dataframe? [duplicate]

The code is like the following: ...
1
vote
1answer
2k views

Adding a quadratic term flips the signs of the coefficients

So I have a question of utilizing quadratic (second order) predictors with GLMs in R. Basically I have three predictor variables $(x, y, z)$ and a response variable (let's call it ozone). $x$, $y$, ...
2
votes
1answer
1k views

Why beta sign is different than correlation sign? [duplicate]

I am trying to interpret the sign of my 5 x-variables against y-variable. The sign of some coefficients in the regression output (command: reg) are different than the signs under correlation matrix (...
3
votes
1answer
655 views

Why after including lags do seasonal dummies become significant?

I am trying to model data that clearly looks like it has seasons. However I only pick up seasonality in very small subsets of the data and only after I add in lagged variables and eliminate trend. I ...
3
votes
1answer
89 views

Any examples of generalisation of simpson's paradox to other metrics

Simpson's paradox is introduced on wikipedia using 'metrics' of success rates and regression coefficients, for the first (success rate of kidney stone treatments): How to resolve Simpson's paradox?...
1
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1answer
105 views

Cox model on bank customers

I am setting up a Cox model, to model the probability that new customers leave the bank . I had a nice set up, with plenty of significant explanatory variables, until the result of one categorical ...
0
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0answers
158 views

Why does adding independent variables make all independent variables insignificant? [duplicate]

I am running a logistic regression in Stata to try to determine which independent variables potentially cause a response in a binary dependent variable. When I run certain independent variables ...
1
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0answers
153 views

Negative Binomial Regression with variable constraint?

I am working with a negative binomial regression. The data frame contains 38 predictors and 48 records. After variable selection I used only 8 variables. Finally, I got some good results. Here is the ...

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