Najib Mozahem – Understanding Regression Techniques

Description

Najib Mozahem – Understanding Regression Techniques

What you’ll learn

Understand what regression is
Build linear regression models
Build logistic regression models
Build count models
Interpret regression results
Visualise the results
Test model assumptions

Get Najib Mozahem – Understanding Regression Techniques download

Course content
Expand all 89 lectures07:10:05
–Simple Linear Regression
33:36
Introduction
Preview
03:15
Simple linear regression
Preview
04:26
The slope
05:29
R-squared
05:30
The p-value
07:06
Model fit
Preview
02:34
The residuals
05:16
+Multiple linear regression
5 lectures13:11
+Linear Regression: Binary, Categorical, and Quadratic Variables
3 lectures27:30
+Linear Regression: Checking Model Fit and Assumptions
8 lectures31:25
+Linear Regression Case Study
11 lectures45:35
+Logistic Regression: Contingency Tables
4 lectures17:17
+Logistic Regression Models
7 lectures49:37
+Logistic Regression: Prediction and Model Fit
7 lectures27:19
+Logistic Regression Case Study
12 lectures49:31
+Count Models: Count Tables
4 lectures11:20
5 more sections
Requirements
none

Description

Included in this course is an e-book and a set of slides. The purpose of the course is to introduce the students to regression techniques. The course covers linear regression, logistic regression and count model regression. The theory behind each of these three techniques is described in an intuitive and non-mathematical way. Students will learn when to use each of these three techniques, how to test the assumptions, how to build models, how to assess the goodness-of-fit of the models, and how to interpret the results. The course does not assume the use of any specific statistical software. Therefore, this course should be of use to anyone intending on applying regression techniques no matter which software they use. The course also walks students through three detailed case studies.
Who this course is for:

Beginner data science students
Business statistics students