# Logistic Regression Using Stata [Video]

 Learn Create contingency tables Grasp the concept of logistic regression Understand the output produced by logistic regression Calculate the odds ratio Discover when to use logistic regression Apply logistic regression using Stata Work with categorical variables for statistical analysis 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, youâ€™ll learn how to interpret the odds and calculate the odds ratios. Youâ€™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, youâ€™ll be taken through a real-world project to understand and implement various commands. By the end of this course, youâ€™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 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 3 hours 37 minutes 9781800205987 30 Mar 2020
 Introduction Two-by-two tables The odds The odds ratio Two-by-three tables
 Single independent variable Examples Binary variables Multiple independent variables Categorical variables Nonlinearity: Non-graphical test Nonlinearity: Graphical test
 Introduction to the dataset Continuous variables Test of linearity: Non-graphical Test of linearity: Graphical Quadratic terms Binary variables Categorical variables: Part 1 Categorical variables: Part 2 Multivariate analysis