# Tag Info

### Linear regression's (OLS) coefficient interpretation with heteroscedasticity

Heteroscedasticity makes it so that the OLS estimator is not the best linear unbiased estimator of the regression slopes and makes it so that the usual standard errors (and the quantities based on ...
• 21.9k

### QQ plot result doesn't correspond to normality test

Your Q-Q plot does not show that the data is normally distributed. In fact, it shows that the distribution diverges from Gaussian (values lower than -1 and higher than 1.5 diverge from the diagonal ...
• 113k
Accepted

### How to assess normality under the OLS assumptions?

The first two are totally wrong but are common misconceptions about the normality assumption in OLS regression (when we choose to make such an assumption, which we don’t have to do). There is no ...
• 31.1k

### Exists an option to avoid reference categories in logistic regression?

This question has nothing to do with logistic regression per se, the problem and answers is the same for all generalized linear regression models. If you have only one categorical variable, just leave ...
• 64.9k

### QQ plot result doesn't correspond to normality test

Just on first look, this distribution looks very short tailed, as you can see it looks kind of like this simulation with a uniform distribution

### Exists an option to avoid reference categories in logistic regression?

Normally, with three categories, you will obtain an intercept, reflecting the log odds of the outcome in the reference category, and two effect terms, indicating how the log odds for the other two ...
• 4,691

### Interpreting interaction effects for categorical reference group in regression

@EdM makes valid points - read those first. Just for reference, you could get the effects of interest along with their confidence intervals using the emmeans package. For example: ...
• 3,110

### Interpreting interaction effects for categorical reference group in regression

First, with the default R treatment coding of your categorical predictors, the individual coefficients for things like Story Vision are their associations with ...
• 62.3k

### Different Meanings of "Clusters" in Statistics

From the Merriam-Webster Dictionary: a number of similar things that occur together The two uses of the term that you describe have to do whether you are trying to discover a cluster in a data set ...
• 62.3k
Accepted

### Logistic regression simulation with respect to event occurrence (prevalence)

You have an array of explanatory variables $(x_1, x_2, \ldots, x_n)$ ($n=20000$) and a model that assigns a probability to each $x_i.$ You seek a subarray of these variables that has a mean ...
• 287k

### What is the impact of duplicate data on the variance of regression coefficient?

The coefficients themselves will no change. Imagine you perform the analysis on the first dataset, and plot the regression line with the datapoints around the regression line. Now what would happen if ...
• 570

### Forecasting using regression coefficients

Your first question: Yes, this is a valid approach. If you only want to do prediction and you think a linear dependency is appropriate, this is a valid approach. Your second question: Of course, you ...
• 2,958

### Cox Proportional Hazards : Why not "Cox Proportional Survival"?

Proportional survival rates depend on the overall prevalence of events, proportional hazards do not. Suppose you have two groups with different hazard rates, one where events occur in 10% of the ...
• 5,743
Accepted

### What does it mean when there is a pattern in residuals related to the dependent variable?

It usually means nothing -- and that's why we don't ordinarily look at this plot. A regression model fits values $\hat y$ to responses $y.$ We can analyze the response into the sum of the fitted ...
• 287k
Accepted

1 vote

### How do I interpret the coefficients of a mixed effects multilevel logistic regression differently from regular logistic model?

@Robert Long what would it mean if a variable was significant in the regular logistic regression, but no longer significant after a random effect is added in the mixed-effect model? What does that say ...
• 41
1 vote

### "Survival" vs. "Hazard" : When to Use Which?

Hazard models are highly flexible methods that produce summary measures of differences in the time to event. Using the hazard function, we can create multivariable regression models with independent ...
• 1,743
1 vote

### Testing difference between coefficients of nonlinear regression models

I feel a bit bad by overcrowding the comments section with pedantic notes and questions about the origins of the noise in the data. So I will make up for it with an answer that is a bit more decent ...
• 46.5k

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