Join Conrad Carlberg for an in-depth discussion in this video What you should know, part of Logistic Regression in R and Excel.
- [Instructor] There are a couple of preliminaries that you probably ought to see to before you take this course on Logistic Regression. There are a couple basics, one of them is you should know how to install and use R and its packages. And you should also have some background, at least, in least-squares regression analysis. Now if you don't have those clubs in your bag, there are a couple of courses that you might want to take a look at to get up to speed. One of them is Up and Running with R, which does a good job of telling you how to install R and how to install and use its various packages.
The other one is R for Excel Users, which has a fair amount of content involving the analysis of least-squares.
Learn how to use R and Excel to analyze data in this course with Conrad Carlberg. He takes you through advanced logistic regression, starting with odds and logarithms and then moving on into binomial distribution and converting predicted odds back to probabilities. After this foundation is established, he shifts the focus to inferential statistics, likelihood ratios, and multinomial regression. Conrad's comprehensive coverage of how to perform logistic regression includes tackling common problems, explaining relationships, reviewing outcomes, and interpreting results.
- Recognizing the problems with ordinary regression on a binary outcome
- Quantifying errors in forecasts
- Managing different slopes
- Forecasting odds instead of probabilities
- Limiting probabilities on the upside and downside
- Working with exponents and bases
- Predicting the logit
- Working with original data and coefficients
- Establishing the Log Likelihood
- Interpreting -2LL or deviance
- Establishing a data frame with XLGetRange
- Using the R functions mlogit or and glm
- Understanding long versus wide shapes in data sets