Join Conrad Carlberg for an in-depth discussion in this video Exercise files, part of Logistic Regression in R and Excel.
- [Instructor] Now this course includes a variety of exercise files and you can use 'em in different kinds of ways, one is just to follow along with the steps that I show you in the video so that you can actually create R analysis and excel workbooks that conform to what you see on the screen. You can also use them to find out more about functions, Excel has an awful lot of worksheet functions, over 300 for example the log natural function and R has quite a few functions as well such as mlogit and GLM, you'll be able to find out more about functions that I use in the course as well as related functions if you do follow along using the exercise files, and you can also use the exercise files as a platform or jumping off place to explore more involved ways, more sophisticated methods to apply using logistic regression analysis.
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