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Regression Modeling in Practice

Approx. 11 hours to complete

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Course Summary

Learn the basics of regression modeling and how to apply it in practice using R programming language. This course covers various regression techniques such as linear, logistic, and multiple regression models.

Key Learning Points

Learn how to apply regression modeling in practice using R programming language
Understand the basic concepts of regression modeling and its various techniques
Gain hands-on experience in building different types of regression models

Learning Outcomes

Ability to apply regression modeling in practice using R programming language
Understanding of different regression techniques
Hands-on experience in building regression models

Prerequisites or good to have knowledge before taking this course

Basic knowledge of statistics
Familiarity with R programming language

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

Similar Courses

Applied Data Science: Machine Learning
Data Science: Statistics and Machine Learning
Regression Analysis

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Description

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.

Outline

Introduction to Regression
Lesson 1: Observational Data
Lesson 2: Experimental Data
Lesson 3: Confounding Variables
Lesson 4: Introduction to Multivariate Methods
Some Guidance for Learners New to the Specialization
Getting Set up for Assignments
Tumblr Instructions
How to Write About Data
Writing About Your Data: Example Assignment

Basics of Linear Regression
SAS Lesson 1: More on Confounding Variables
SAS Lesson 2: Testing a Basic Linear Regression Mode
SAS Lesson 3: Categorical Explanatory Variables
Python Lesson 1: More on Confounding Variables
Python Lesson 2: Testing a Basic Linear Regression Model
Python Lesson 3: Categorical Explanatory Variables
Lesson 4: Linear Regression Assumptions
Lesson 5: Centering Explanatory Variables
SAS or Python - Which to Choose?
Getting Started with SAS
Getting Started with Python
Course Codebooks
Course Data Sets
Uploading Your Own Data to SAS
SAS Program Code for Video Examples
Python Program Code for Video Examples
Outlier Decision Tree

Multiple Regression
SAS Lesson 1: Multiple Regression
SAS Lesson 2: Confidence Intervals
SAS Lesson 3: Polynomial Regression
SAS Lesson 4: Evaluating Model Fit, pt. 1
SAS Lesson 5: Evaluating Model Fit, pt. 2
Python Lesson 1: Multiple Regression
Python Lesson 2: Confidence Intervals
Python Lesson 3: Polynomial Regression
Python Lesson 4: Evaluating Model Fit, pt. 1
Python Lesson 5: Evaluating Model Fit, pt. 2
SAS Program Code for Video Examples
Python Program Code for Video Examples

Logistic Regression
SAS Lesson 1: Categorical Explanatory Variables with More Than Two Categories
Python Lesson 1: Categorical Explanatory Variables with More Than Two Categories
Lesson 2: A Few Things to Keep in Mind
SAS Lesson 3: Logistic Regression for a Binary Response Variable, pt 1
SAS Lesson 4: Logistic Regression for a Binary Response Variable, pt. 2
Python Lesson 3: Logistic Regression for a Binary Response Variable, pt. 1
Python Lesson 4: Logistic Regression for a Binary Response Variable, pt. 2
SAS Program Code for Video Examples
Python Program Code for Video Examples
Week 1 Video Credits
Week 2 Video Credits
Week 3 Video Credits
Week 4 Video Credits

Summary of User Reviews

The Regression Modeling Practice course on Coursera is highly recommended by learners. They praise the course instructor, the practical approach of the course, and the opportunity to apply the concepts learned in real-world scenarios. The course has an impressive rating from learners.

Key Aspect Users Liked About This Course

The course's practical approach has been highly praised by users.

Pros from User Reviews

The instructor is knowledgeable and engaging
The course provides hands-on experience in applying regression models
The course is well-structured and easy to follow
Learners have access to real-world datasets to practice on
The course provides a strong foundation in regression modeling

Cons from User Reviews

Some learners found the content too basic
The course may not be suitable for those without a strong math background
Some learners felt that the pace of the course was too slow
There is a lack of interaction with the instructor
The course does not cover advanced topics in regression modeling

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