Learn how to perform logistic regression using R and Excel. This course shows how to process, analyze, and finalize forecasts and outcomes.
- [Instructor] Welcome to Learning Logistic Regression in R and Excel. Now if you take this course, here's what you can expect to learn. The characteristics of binomial outcome variables, such as buys versus doesn't buy, and recovers or get worse, any kind of situation which you might have to make a decision between two or more alternatives is an appropriate kind of context for this course. You'll also learn about the reasons that binomial outcome variables create problems for standard analysis procedures such as simple and multiple regression.
You'll see how simple transformations that involve odds ratios and logarithms can solve the problems that binomial outcomes bring to regression analysis. Now I use Excel in this course mainly to show you what goes on inside logistic regression's black box. You'll be able to see the intermediate calculations involving those odds and those logarithms, and I use R to show you how to reach the end point of the analysis quickly so that you can skip over those intermediate kinds of calculations.
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