Logistic Regression using Stata

Logistic Regression using Stata, available at $54.99, has an average rating of 4.6, with 40 lectures, based on 153 reviews, and has 1903 subscribers.

You will learn about Create contingency tables Calculate odds ratio Understand what is logistic regression Identify when logistic regression is used Understand the output produced by logistic regression Include categorical variables Test for linearity Predict probabilities Test model fit Apply logistic regression using Stata Visualise the best-fit model This course is ideal for individuals who are Beginner non-mathematical students who want to use logistic regression It is particularly useful for Beginner non-mathematical students who want to use logistic regression.

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Summary

Title: Logistic Regression using Stata

Price: $54.99

Average Rating: 4.6

Number of Lectures: 40

Number of Published Lectures: 40

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 40

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

Create contingency tables
Calculate odds ratio
Understand what is logistic regression
Identify when logistic regression is used
Understand the output produced by logistic regression
Include categorical variables
Test for linearity
Predict probabilities
Test model fit
Apply logistic regression using Stata
Visualise the best-fit model

Who Should Attend

Beginner non-mathematical students who want to use logistic regression

Target Audiences

Beginner non-mathematical students who want to use logistic regression

Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind logistic regression. The theory is explained in an intuitive way. The math is kept to a minimum. The course starts with an introduction to contingency tables, in which students learn how to calculate and interpret the odds and the odds ratios. From there, the course moves on to the topic of logistic regression, where students will learn when and how to use this regression technique. Topics such as model building, prediction, and assessment of model fit are covered. In addition, the course also covers diagnostics by covering the topics of residuals and influential observations.

In the second part of the course, students learn how to apply what they learned using Stata. In this part, students will walk through a large project in order to understand the type of questions that are raised throughout the process, and which commands to use in order to address these questions.

Course Curriculum

Chapter 1: Contingency Tables

Lecture 1: Introduction

Lecture 2: Two-by-two tables

Lecture 3: The odds

Lecture 4: The odds ratio

Lecture 5: Two-by-three tables

Chapter 2: Logistic Regression

Lecture 1: Single independent variable

Lecture 2: Examples

Lecture 3: Binary variables

Lecture 4: Multiple independent variables

Lecture 5: Categorical variables

Lecture 6: Nonlinearity: Non-graphical test

Lecture 7: Nonlinearity: Graphical test

Chapter 3: Prediction and Model Fit

Lecture 1: Prediction

Lecture 2: Goodness of fit: Likelihood ratio test

Lecture 3: Goodness of fit: Hosmer-Lemeshow test

Lecture 4: Goodness of fit: Classification tables

Lecture 5: Goodness of fit: ROC analysis

Lecture 6: Residuals

Lecture 7: Influential Observations

Chapter 4: Application: Fitting the Model

Lecture 1: Introduction to the dataset

Lecture 2: Continuous variables

Lecture 3: Test of linearity: Non-graphical

Lecture 4: Test of linearity: Graphical

Lecture 5: Quadratic terms

Lecture 6: Binary variables

Lecture 7: Categorical variables: Part 1

Lecture 8: Categorical variables: Part 2

Lecture 9: Multivariate analysis

Chapter 5: Application: Model Fit

Lecture 1: Goodness of fit: Likelihood ratio test

Lecture 2: Goodness of fit: Hosmer-Lemeshow test

Lecture 3: Goodness of fit: Classification tables

Lecture 4: Goodness of fit: ROC analysis

Lecture 5: Residual analysis

Lecture 6: Influential observations

Lecture 7: Combining both residuals and influence in one graph

Chapter 6: Application: Visualizing the Model

Lecture 1: Introduction

Lecture 2: Non-graphical interpretation

Lecture 3: Graphical interpretation: single variable

Lecture 4: Graphical interpretation: two variables

Chapter 7: Conclusion

Lecture 1: Next step

Instructors

Najib Mozahem
Assistant Professor

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 18 votes
4 stars: 47 votes
5 stars: 87 votes

Frequently Asked Questions

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