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LinkedIn Learning - Logistic Regression in R and Excel [CoursesGhar]

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220.81 MB

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File	Size
!! IMPORTANT Note !!.txt	287.00 B
!!! Please Support !!! [CoursesGhar.Com].txt	197.00 B
00. Websites You May Like/CoursesGhar.Com.url	114.00 B
00. Websites You May Like/New Internet Shortcut.url	114.00 B
telegram @coursesghargate.url	128.00 B
Uploaded by [Coursesghar.com].txt	1.10 kB
Visit coursesghar.com for more awesome tutorials.url	114.00 B
[1] Introduction/[1] Welcome.mp4	9.98 MB
[1] Introduction/[1] Welcome.srt	1.94 kB
[1] Introduction/[2] What you should know.mp4	1.22 MB
[1] Introduction/[2] What you should know.srt	1.43 kB
[1] Introduction/[3] Exercise files.mp4	2.26 MB
[1] Introduction/[3] Exercise files.srt	1.44 kB
[2] 1. Ordinary Regression and Nominal Outcome Variables/[1] The normality assumption.mp4	12.82 MB
[2] 1. Ordinary Regression and Nominal Outcome Variables/[1] The normality assumption.srt	8.66 kB
[2] 1. Ordinary Regression and Nominal Outcome Variables/[2] Recognize abnormal distribution.mp4	11.96 MB
[2] 1. Ordinary Regression and Nominal Outcome Variables/[2] Recognize abnormal distribution.srt	7.60 kB
[2] 1. Ordinary Regression and Nominal Outcome Variables/[3] Forecast Too high or too low.mp4	10.63 MB
[2] 1. Ordinary Regression and Nominal Outcome Variables/[3] Forecast Too high or too low.srt	7.42 kB
[2] 1. Ordinary Regression and Nominal Outcome Variables/[4] Manage different slopes.mp4	7.78 MB
[2] 1. Ordinary Regression and Nominal Outcome Variables/[4] Manage different slopes.srt	5.20 kB
[3] 2. Solutions to Problems with Ordinary Regression/[1] Use of odds instead of probabilities.mp4	6.28 MB
[3] 2. Solutions to Problems with Ordinary Regression/[1] Use of odds instead of probabilities.srt	4.31 kB
[3] 2. Solutions to Problems with Ordinary Regression/[2] Use of odds to limit the probabilities on the upside.mp4	3.21 MB
[3] 2. Solutions to Problems with Ordinary Regression/[2] Use of odds to limit the probabilities on the upside.srt	2.05 kB
[3] 2. Solutions to Problems with Ordinary Regression/[3] Logs exponents, bases, sum of logs, and the log of products.mp4	8.07 MB
[3] 2. Solutions to Problems with Ordinary Regression/[3] Logs exponents, bases, sum of logs, and the log of products.srt	5.04 kB
[3] 2. Solutions to Problems with Ordinary Regression/[4] Use of log odds to limit the probabilities on the downside.mp4	8.04 MB
[3] 2. Solutions to Problems with Ordinary Regression/[4] Use of log odds to limit the probabilities on the downside.srt	5.16 kB
[3] 2. Solutions to Problems with Ordinary Regression/[5] Predict the log of the odds, the logit.mp4	12.30 MB
[3] 2. Solutions to Problems with Ordinary Regression/[5] Predict the log of the odds, the logit.srt	7.61 kB
[4] 3. Running a Logistic Regression in Excel/[3] Establish the log likelihood and run Solver.mp4	18.40 MB
[4] 3. Running a Logistic Regression in Excel/[3] Establish the log likelihood and run Solver.srt	11.53 kB
[4] 3. Running a Logistic Regression in Excel/[4] Interpret -2LL or deviance.mp4	13.66 MB
[4] 3. Running a Logistic Regression in Excel/[4] Interpret -2LL or deviance.srt	8.06 kB
[5] 4. Running a Binomial Logistic Regression in R/[1] Install the mlogit package.mp4	12.76 MB
[5] 4. Running a Binomial Logistic Regression in R/[1] Install the mlogit package.srt	7.11 kB
[5] 4. Running a Binomial Logistic Regression in R/[2] Establish the data frame with XLGetRange.mp4	9.94 MB
[5] 4. Running a Binomial Logistic Regression in R/[2] Establish the data frame with XLGetRange.srt	6.29 kB
[5] 4. Running a Binomial Logistic Regression in R/[3] The mlogit function syntax.mp4	24.37 MB
[5] 4. Running a Binomial Logistic Regression in R/[3] The mlogit function syntax.srt	17.34 kB
[5] 4. Running a Binomial Logistic Regression in R/[4] Use of glm instead of mlogit.mp4	8.20 MB
[5] 4. Running a Binomial Logistic Regression in R/[4] Use of glm instead of mlogit.srt	4.46 kB
[6] 5. Running a Multinomial Logistic Regression in R/[2] Identify long versus wide data frames.mp4	16.40 MB
[6] 5. Running a Multinomial Logistic Regression in R/[2] Identify long versus wide data frames.srt	9.16 kB
[6] 5. Running a Multinomial Logistic Regression in R/[3] The special mlogit syntax.mp4	20.60 MB
[6] 5. Running a Multinomial Logistic Regression in R/[3] The special mlogit syntax.srt	13.27 kB
[7] Conclusion/[1] Next steps [CoursesGhar.Com].mp4	1.79 MB
[7] Conclusion/[1] Next steps [CoursesGhar.Com].srt	1.22 kB

Description

LinkedIn Learning - Logistic Regression in R and Excel [CoursesGhar]

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.
Learning objectives
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