Logistic Regression Using Stata

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Najib Mozahem

3:36:44

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1.Contingency Tables

01.Introduction.mp4

02:21

02.Two-by-two tables.mp4

04:03

03.The odds.mp4

03:21

04.The odds ratio.mp4

02:37

05.Two-by-three tables.mp4

07:16

2.Logistic Regression

06.Single independent variable.mp4

12:51

07.Examples.mp4

05:06

08.Binary variables.mp4

06:30

09.Multiple independent variables.mp4

05:39

10.Categorical variables.mp4

08:34

11.Nonlinearity - Non-graphical test.mp4

04:06

12.Nonlinearity - Graphical test.mp4

06:51

3.Prediction and Model Fit

13.Prediction.mp4

03:58

14.Goodness of fit - Likelihood ratio test.mp4

02:04

15.Goodness of fit - Hosmer-Lemeshow test.mp4

03:44

16.Goodness of fit - Classification tables.mp4

08:28

17.Goodness of fit - ROC analysis.mp4

01:41

18.Residuals.mp4

02:23

19.Influential Observations.mp4

05:00

4.Application - Fitting the Model

20.Introduction to the dataset.mp4

03:23

21.Continuous variables.mp4

05:23

22.Test of linearity - Non-graphical.mp4

02:01

23.Test of linearity - Graphical.mp4

11:57

24.Quadratic terms.mp4

07:43

25.Binary variables.mp4

03:55

26.Categorical variables - Part 1.mp4

10:03

27.Categorical variables - Part 2.mp4

06:37

28.Multivariate analysis.mp4

02:11

5.Application - Model Fit

29.Goodness of fit - Likelihood ratio test.mp4

01:15

30.Goodness of fit - Hosmer-Lemeshow test.mp4

01:35

31.Goodness of fit - Classification tables.mp4

08:03

32.Goodness of fit - ROC analysis.mp4

00:52

33.Residual analysis.mp4

07:37

34.Influential observations.mp4

04:58

35.Combining both residuals and influence in one graph.mp4

10:57

6.Application - Visualizing the Model

36.Introduction.mp4

03:05

37.Non-graphical interpretation.mp4

10:46

38.Graphical interpretation - single variable.mp4

10:13

39.Graphical interpretation - two variables.mp4

05:39

7.Conclusion

40.Next step.mp4

01:58

Description

Stata is one of the leading statistical software packages widely used in different fields. This course is divided into two parts. The first part covers the theory behind logistic regression, and the second part enables you to apply the theory to practical scenarios using Stata.

Starting with an introduction to contingency tables, you’ll learn how to interpret the odds and calculate the odds ratios. You’ll then understand when and how to use the logistic regression technique. The course covers topics such as model building, prediction, and assessment of model fit. Additionally, it will help you get to grips with diagnostics by explaining the concept of residuals and influential observations. As you advance, you’ll be taken through a real-world project to understand and implement various commands.

By the end of this course, you’ll have all the knowledge you need to apply logistic regression for descriptive statistics.

All codes and supporting files are available at-

https://github.com/PacktPublishing/Logistic-Regression-using-Stata

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Regression Analysis

Stata

Najib Mozahem

Instructor's Courses

Najib Mozahem works as a researcher and as an assistant professor at the university level, where he teaches Quantitative Analysis. He holds a Bachelor’s degree in Computer and Communication Engineering, completed his MBA with distinction, and completed his Ph.D. in Organizational Theory where he won the best thesis prize for Ph.D. He has also received the teaching excellence award for the year 2016 – 2017. His research interests include quantitative modeling and the study of human behavior in organizations.

PacktPub

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.