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  • Date: 17-05-2017, 07:16
17-05-2017, 07:16

Lynda - Logistic Regression in R and Excel 2017 TUTORiAL

Category: Tutorials

Lynda - Logistic Regression in R and Excel 2017 TUTORiAL
Lynda - Logistic Regression in R and Excel 2017 TUTORiAL | 297 MB

Business decisions are often binary: take on this project or put it off for a year; extend credit to this customer or insist on cash; open a new retail outlet in a particular location or find another spot. When an outcome is a continuous variable such as revenue, ordinary regression is often a good technique, but when there are only two outcomes, logistic regression usually offers better tools.
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.


00 - Introduction
Welcome
What you should know
Exercise files
01 - Ordinary Regression and Nominal Outcome Variables
The normality assumption
Recognize abnormal distribution
Forecast: Too high or too low
Manage different slopes
02 - 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
03 - 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
04 - 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
05 - Running a Multinomial Logistic Regressions in R
Deal with problems introduced by three or more possible outcomes
Identify long versus wide data frames
The special mlogit syntax
06 - Conclusion
Next steps
Exercise Files

lynda.com/Office-365-Mac-tutorials/Learning-Logistic-Regression-R-Excel




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Tags Cloud: Lynda, Logistic, Regression, and, Excel

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