Can a highly skewed variable be used in logistic regression?

Can a highly skewed variable be used in logistic regression?

The description of the variable is listed below: The data is highly right skewed. As far as I understand, logistic regression doesn’t really care about normality. However, with highly skewed data like this, should I not transform the data? or is it not required?

How to interpret logistic regression coefficients for beginners?

Interpret Logistic Regression Coefficients [For Beginners] By George Choueiry – PharmD, MPH The logistic regression coefficient β is the change in log odds of having the outcome per unit change in the predictor X. So increasing the predictor by 1 unit (or going from 1 level to the next) multiplies the odds of having the outcome by eβ.

Do you care about normality in logistic regression?

The data is highly right skewed. As far as I understand, logistic regression doesn’t really care about normality. However, with highly skewed data like this, should I not transform the data? or is it not required? And if I should transform, how do I know which transformation to use, and how to interpret it?

When to use Hosmer-Lemeshow test in logistic regression?

When the data have few trials per row, the Hosmer-Lemeshow test is a more trustworthy indicator of how well the model fits the data. In these results, the goodness-of-fit tests are all greater than the significance level of 0.05, which indicates that there is not enough evidence to conclude that the model does not fit the data.

Is there residual skew in the Gaussian regression model?

This residual skewness remains even if many covariates are incorporated. Therefore, a simple refinement of the classical nonhomogeneous Gaussian regression model is proposed to overcome this problem by assuming a skewed response distribution to account for possible skewness.

Which is the skewed logistic response for 2 m temperature?

This study shows a comprehensive analysis of the performance of nonhomogeneous post-processing for the 2 m temperature for three different site types, comparing Gaussian, logistic, and skewed logistic response distributions.

Why do histograms indicate skewed residual distribution?

More specifically, the histograms indicate skewness in the residual distribution. As a marginal Gaussian model without covariates can already exhibit skewness for temperature data ( Toth and Szentimrey , 1990; Warwick and Curran , 1993; Harmel et al. , 2002), skewness is supposed to vanish if covariates are incorporated.

What are the diagnostic statistics in logistic regression?

So far, we have seen the basic three diagnostic statistics: the Pearson residual, the deviance residual and the leverage (the hat value). They are the basic building blocks in logistic regression diagnostics. There are other diagnostic statistics that are used for different purposes.

When to use specification error in logistic regression?

3.1 Specification Error. When we build a logistic regression model, we assume that the logit of the outcome variable is a linear combination of the independent variables. This involves two aspects, as we are dealing with the two sides of our logistic regression equation.