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LinkedIn Learning

Logistic Regression in R and Excel

via LinkedIn Learning

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

Taught by

Conrad Carlberg

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