Understanding Regression Techniques

Understanding Regression Techniques, available at $49.99, has an average rating of 4.5, with 89 lectures, based on 133 reviews, and has 3549 subscribers.

You will learn about Understand what regression is Build linear regression models Build logistic regression models Build count models Interpret regression results Visualise the results Test model assumptions This course is ideal for individuals who are Beginner data science students or Business statistics students It is particularly useful for Beginner data science students or Business statistics students.

Enroll now: Understanding Regression Techniques

Summary

Title: Understanding Regression Techniques

Price: $49.99

Average Rating: 4.5

Number of Lectures: 89

Number of Published Lectures: 89

Number of Curriculum Items: 89

Number of Published Curriculum Objects: 89

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Beginner data science students

Business statistics students

Target Audiences

Beginner data science students

Business statistics students

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.

Course Curriculum

Chapter 1: Simple Linear Regression

Lecture 1: Introduction

Lecture 2: Simple linear regression

Lecture 3: The slope

Lecture 4: R-squared

Lecture 5: The p-value

Lecture 6: Model fit

Lecture 7: The residuals

Chapter 2: Multiple linear regression

Lecture 1: Multiple linear regression

Lecture 2: The slopes

Lecture 3: R-squared

Lecture 4: The p-value

Lecture 5: Model fit and residuals

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

Lecture 1: Binary variables

Lecture 2: Categrical variables

Lecture 3: Quadratic variables

Chapter 4: Linear Regression: Checking Model Fit and Assumptions

Lecture 1: Prediction

Lecture 2: Normality of residuals

Lecture 3: Independence of residuals

Lecture 4: Constant variance

Lecture 5: Multicolinearity

Lecture 6: Outliers

Lecture 7: Influencial observations

Lecture 8: Selection algorithms

Chapter 5: Linear Regression Case Study

Lecture 1: The dataset

Lecture 2: Including continuous variables

Lecture 3: Including binary variables

Lecture 4: Including categorical variables

Lecture 5: Multiple regression

Lecture 6: Checking model fit

Lecture 7: Checking model assumptions

Lecture 8: Multicollinearity

Lecture 9: Outliers

Lecture 10: Influential observations

Lecture 11: Visualizing the result

Chapter 6: Logistic Regression: Contingency Tables

Lecture 1: Two-by-two tables

Lecture 2: The odds

Lecture 3: The odds ratio

Lecture 4: Two-by-three tables

Chapter 7: Logistic Regression Models

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 8: Logistic Regression: 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 9: Logistic Regression Case Study

Lecture 1: The dataset

Lecture 2: Continuous variables

Lecture 3: Test of linearity: Non-graphical

Lecture 4: Test of linearity: Graphical

Lecture 5: Binary variables

Lecture 6: Categorical variables

Lecture 7: Multivariate analysis

Lecture 8: Goodness of fit

Lecture 9: Residual analysis

Lecture 10: Influential observations

Lecture 11: Combining both residuals and influence in one graph

Lecture 12: Visualizing the result

Chapter 10: Count Models: Count Tables

Lecture 1: Count tables

Lecture 2: Risk

Lecture 3: Inceidence-rate ratio

Lecture 4: Two-by-three tables

Chapter 11: Poisson Regression

Lecture 1: Single independent variable

Lecture 2: Examples

Lecture 3: Binary variables

Lecture 4: Multiple independent variables

Lecture 5: Categorical variables

Lecture 6: Exposure

Chapter 12: Other Count Models

Lecture 1: Negative binomial regression

Lecture 2: Truncated models

Lecture 3: Zero-inflated models

Lecture 4: Comparison of models

Chapter 13: Prediction

Lecture 1: Predicting the number of events

Lecture 2: Predicting probabilities of certain counts

Chapter 14: Count Model Case Study

Lecture 1: The dataset

Lecture 2: Continuous variables

Lecture 3: Binary variables

Lecture 4: Multivariate analysis

Lecture 5: Negative binomial regression

Lecture 6: Zero-inflated models

Instructors

Najib Mozahem
Assistant Professor

Rating Distribution

1 stars: 1 votes

2 stars: 4 votes

3 stars: 15 votes

4 stars: 48 votes

5 stars: 65 votes

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