Video description

Leverage Stata to estimate descriptive statistics using logistic regression

• Understand the theory behind logistic regression in detail
• Explore different goodness of fit tests including likelihood ratio test and Hosmer-Lemeshow test
• Get to grips with the fundamentals by applying them in a practical project

In Detail

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.

Publisher resources

1. Chapter 1 : Contingency Tables
1. Introduction 00:02:21
2. Two-by-two tables 00:04:03
3. The odds 00:03:21
4. The odds ratio 00:02:38
5. Two-by-three tables 00:07:17
2. Chapter 2 : Logistic Regression
1. Single independent variable 00:12:51
2. Examples 00:05:06
3. Binary variables 00:06:30
4. Multiple independent variables 00:05:40
5. Categorical variables 00:08:34
6. Nonlinearity: Non-graphical test 00:04:07
7. Nonlinearity: Graphical test 00:06:52
3. Chapter 3 : Prediction and Model Fit
1. Prediction 00:03:58
2. Goodness of fit: Likelihood ratio test 00:02:05
3. Goodness of fit: Hosmer-Lemeshow test 00:03:45
4. Goodness of fit: Classification tables 00:08:29
5. Goodness of fit: ROC analysis 00:01:42
6. Residuals 00:02:23
7. Influential Observations 00:05:01
4. Chapter 4 : Application: Fitting the Model
1. Introduction to the dataset 00:03:24
2. Continuous variables 00:05:24
3. Test of linearity: Non-graphical 00:02:01
4. Test of linearity: Graphical 00:11:58
6. Binary variables 00:03:55
7. Categorical variables: Part 1 00:10:04
8. Categorical variables: Part 2 00:06:37
9. Multivariate analysis 00:02:12
5. Chapter 5 : Application: Model Fit
1. Goodness of fit: Likelihood ratio test 00:01:16
2. Goodness of fit: Hosmer-Lemeshow test 00:01:36
3. Goodness of fit: Classification tables 00:08:03
4. Goodness of fit: ROC analysis 00:00:53
5. Residual analysis 00:07:38
6. Influential observations 00:04:59
7. Combining both residuals and influence in one graph 00:10:57
6. Chapter 6 : Application: Visualizing the Model
1. Introduction 00:03:05
2. Non-graphical interpretation 00:10:47
3. Graphical interpretation: single variable 00:10:14
4. Graphical interpretation: two variables 00:05:39
7. Chapter 7 : Conclusion
1. Next step 00:01:58

Product information

• Title: Logistic Regression Using Stata
• Author(s): Najib Mozahem
• Release date: March 2020
• Publisher(s): Packt Publishing
• ISBN: 9781800205987