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 [1] Introduction

  [1] Welcome.srt

  [2] What you should know.srt

  [3] Exercise files.srt

 [2] 1. Ordinary Regression and Nominal Outcome Variables

  [1] The normality assumption.srt

  [2] Recognize abnormal distribution.srt

  [3] Forecast Too high or too low.srt

  [4] Manage different slopes.srt

 [3] 2. Solutions to Problems with Ordinary Regression

  [1] Use of odds instead of probabilities.srt

  [2] Use of odds to limit the probabilities on the upside.srt

  [3] Logs exponents, bases, sum of logs, and the log of products.srt

  [4] Use of log odds to limit the probabilities on the downside.srt

  [5] Predict the log of the odds, the logit.srt

 [4] 3. Running a Logistic Regression in Excel

  [3] Establish the log likelihood and run Solver.srt

  [4] Interpret -2LL or deviance.srt

 [5] 4. Running a Binomial Logistic Regression in R

  [1] Install the mlogit package.srt

  [2] Establish the data frame with XLGetRange.srt

  [3] The mlogit function syntax.srt

  [4] Use of glm instead of mlogit.srt

 [6] 5. Running a Multinomial Logistic Regression in R

  [2] Identify long versus wide data frames.srt

  [3] The special mlogit syntax.srt

 [7] Conclusion

  [1] Next steps [CoursesGhar.Com].srt