: Najib Mozahem
: March 24, 2020
Explore the fundamentals of linear regression, logistic regression, and count model regression in an intuitive and non-mathematical way
7 hours 10 minutes
â€¢ Understand the concept of regression
â€¢ Build logistic regression models
â€¢ Interpret regression results
â€¢ Build linear regression models
â€¢ Build count models
â€¢ Visualize the results
Linear and logistic regressions are among the first set of algorithms youâ€™ll study to get started on your journey in data science.
This course explores three basic regressionsâ€”linear, logistic, and count model. Starting with linear regressions, youâ€™ll first understand the difference between simple and multiple linear regressions and explore different types of variables, including binary, categorical, and quadratic. Once youâ€™ve got to grips with the fundamentals, youâ€™ll apply what youâ€™ve learned to solve a case study. As you advance, youâ€™ll explore logistic regression models and cover variables, non-linearity tests, prediction, and model fit. Finally, youâ€™ll get well-versed with count model regression.
By the end of the course, youâ€™ll be equipped with the knowledge you need to investigate correlations between multiple variables using regression models.
All the codes and supporting files for this course will be available at- https://github.com/PacktPublishing/Understanding-Regression-Techniques
â€¢ Understand the normality and independence of residuals
â€¢ Explore both graphical and non-graphical tests for non-linearity in logistic regression models
â€¢ Get to grips with count tables, their risk, and incidence rate ratio
Najib Mozahem works as a researcher and as an assistant professor at the university level, where he teaches Quantitative Analysis. He holds a Bachelorâ€™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 â€“ 2017. His research interests include quantitative modeling and the study of human behavior in organizations.