Remember all those Z-scores you had to calculate in Intro Stats? Converting a variable to a Z-score is standardizing. In other words, do these steps for Y, your outcome variable, and every X, your predictors: 1. Calculate the mean and standard deviation.
This is especially true when your audience is a clinical one who needs to make decisions based on your results. So you're also absolutely correct that presenting a table full of odds ratios is not the way to go here. To answer your first question, no. You cannot say for every one female who fails, X number of males will fail. You can, however, convey the odds ratios in a…
We have added a worked example to the README of our experimental logistic regression code. The Logistic codebase is designed to support experimentation on variations of logistic regression including: A pure Java implementation (thus directly usable in Java server environments). A simple multinomial implementation (that allows more than two possible result categories). The ability to [...] Related posts: Large Data Logistic Regression (with example Hadoop code) How robust is logistic…
When the response variable for a regression model is categorical, linear models don’t work. Logistic regression is one type of model that does, and it’s relatively straightforward for binary responses. When the response variable is not just categorical, but ordered categories, the model needs to be able to handle the multiple categories, and ideally, account for the ordering.
Related posts: How robust is logistic regression? The equivalence of logistic regression and maximum entropy models What does a generalized linear model do?
Logistic Regression is a popular and effective technique for modeling categorical outcomes as a function of both continuous and categorical variables. The question is: how robust is it? Or: how robust are the common implementations? (note: we are using robust in a more standard English sense of performs well for all inputs, not in the [...] Related posts: The equivalence of logistic regression and maximum entropy models Learn Logistic Regression…
Related posts: How robust is logistic regression? Modeling Trick: Impact Coding of Categorical Variables with Many Levels The Simpler Derivation of Logistic Regression
Like some of the other terms in our list--level and beta--GLM has two different meanings. It's a little different than the others, though, because it's an abbreviation for two different terms: General Linear Model and Generalized Linear Model. It's extra confusing because their names are so similar on top of having the same abbreviation.
Related posts: Modeling Trick: Impact Coding of Categorical Variables with Many Levels The equivalence of logistic regression and maximum entropy models Large Data Logistic Regression (with example Hadoop code)
One of the shortcomings of regression (both linear and logistic) is that it doesn’t handle categorical variables with a very large number of possible values (for example, postal codes). You can get around this, of course, by going to another modeling technique, such as Naive Bayes; however, you lose some of the advantages of regression [...] Related posts: Modeling Trick: Masked Variables A bit more on impact coding Modeling Trick:…
