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# Logistic Regression in R and Excel

## Overview

Learn how to perform logistic regression using R and Excel. This course shows how to process, analyze, and finalize forecasts and outcomes.

## Syllabus

Introduction
• Welcome
• What you should know
• Exercise files
1. Ordinary Regression and Nominal Outcome Variables
• The normality assumption
• Recognize abnormal distribution
• Forecast: Too high or too low
• Manage different slopes
2. Solutions to Problems with Ordinary Regression
• Use of odds instead of probabilities
• Use of odds to limit the probabilities on the upside
• Logs: exponents, bases, sum of logs, and the log of products
• Use of log odds to limit the probabilities on the downside
• Predict the log of the odds, the logit
3. Running a Logistic Regression in Excel
• Set up the worksheet: Original data and logistic regression coefficients
• Set up the logit column, the antilog column, and the probability column
• Establish the log likelihood and run Solver
• Interpret -2LL or deviance
4. Running a Binomial Logistic Regression in R
• Install the mlogit package
• Establish the data frame with XLGetRange
• The mlogit function syntax
• Use of glm instead of mlogit
5. Running a Multinomial Logistic Regression in R
• Deal with problems introduced by three or more possible outcomes
• Identify long versus wide data frames
• The special mlogit syntax
Conclusion
• Next steps