Understanding Regression Techniques

  1. Home
  2. Regression-analysis
  3. Understanding Regression Techniques

Tags: Regression Analysis

An Introduction to Predictive Analytics for Data Scientists

Last updated 2022-01-10 | 4.7

- Understand what regression is
- Build linear regression models
- Build logistic regression models

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

* 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

Course content

15 sections • 89 lectures

Simple Linear Regression 7 lectures

Introduction Preview 03:15

Simple linear regression Preview 04:26

The slope Preview 05:29

R-squared Preview 05:30

The p-value Preview 07:06

Model fit Preview 02:34

The residuals Preview 05:16

Multiple linear regression 5 lectures

Multiple linear regression Preview 03:09

The slopes Preview 05:10

R-squared Preview 01:38

The p-value Preview 01:12

Model fit and residuals Preview 02:02

Linear Regression: Binary, Categorical, and Quadratic Variables 3 lectures

Binary variables Preview 08:37

Categrical variables Preview 11:30

Quadratic variables Preview 07:23

Linear Regression: Checking Model Fit and Assumptions 8 lectures

Prediction Preview 04:27

Normality of residuals Preview 02:20

Independence of residuals Preview 02:25

Constant variance Preview 02:00

Multicolinearity Preview 02:49

Outliers Preview 04:10

Influencial observations Preview 04:33

Selection algorithms Preview 08:41

Linear Regression Case Study 11 lectures

The dataset Preview 03:37

Including continuous variables Preview 10:32

Including binary variables Preview 02:22

Including categorical variables Preview 02:17

Multiple regression Preview 03:51

Checking model fit Preview 02:55

Checking model assumptions Preview 06:41

Multicollinearity Preview 02:06

Outliers Preview 03:27

Influential observations Preview 04:37

Visualizing the result Preview 03:10

Logistic Regression: Contingency Tables 4 lectures

Two-by-two tables Preview 04:03

The odds Preview 03:21

The odds ratio Preview 02:37

Two-by-three tables Preview 07:16

Logistic Regression Models 7 lectures

Single independent variable Preview 12:51

Examples Preview 05:06

Binary variables Preview 06:30

Multiple independent variables Preview 05:39

Categorical variables Preview 08:34

Nonlinearity: Non-graphical test Preview 04:06

Nonlinearity: Graphical test Preview 06:51

Logistic Regression: Prediction and Model Fit 7 lectures

Prediction Preview 03:58

Goodness of fit: Likelihood ratio test Preview 02:04

Goodness of fit: Hosmer-Lemeshow test Preview 03:44

Goodness of fit: Classification tables Preview 08:28

Goodness of fit: ROC analysis Preview 01:41

Residuals Preview 02:23

Influential Observations Preview 05:01

Logistic Regression Case Study 12 lectures

The dataset Preview 03:47

Continuous variables Preview 03:30

Test of linearity: Non-graphical Preview 02:25

Test of linearity: Graphical Preview 05:14

Binary variables Preview 02:41

Categorical variables Preview 08:26

Multivariate analysis Preview 02:25

Goodness of fit Preview 07:00

Residual analysis Preview 03:02

Influential observations Preview 02:51

Combining both residuals and influence in one graph Preview 05:07

Visualizing the result Preview 03:03

Count Models: Count Tables 4 lectures

Count tables Preview 04:07

Risk Preview 02:05

Inceidence-rate ratio Preview 02:35

Two-by-three tables Preview 02:33

Poisson Regression 6 lectures

Single independent variable Preview 16:44

Examples Preview 05:07

Binary variables Preview 06:12

Multiple independent variables Preview 06:31

Categorical variables Preview 08:21

Exposure Preview 08:26

Other Count Models 4 lectures

Negative binomial regression Preview 07:58

Truncated models Preview 04:02

Zero-inflated models Preview 17:31

Comparison of models Preview 07:39

Prediction 2 lectures

Predicting the number of events Preview 02:53

Predicting probabilities of certain counts Preview 02:44

Count Model Case Study 8 lectures

The dataset Preview 01:35

Continuous variables Preview 06:52

Binary variables Preview 01:12

Multivariate analysis Preview 01:10

Negative binomial regression Preview 01:57

Zero-inflated models Preview 07:25

Comparing count models Preview 03:53

Visualizing the result Preview 03:43

Conclusion 1 lectures

Conclusion Preview 01:49