Quantifying Relationships with Regression Models
- Offered byCoursera
- Public/Government Institute
Quantifying Relationships with Regression Models at Coursera Overview
Duration | 11 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Quantifying Relationships with Regression Models at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 5 in the Data Literacy Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 11 hours to complete
- English Subtitles: English
Quantifying Relationships with Regression Models at Coursera Course details
- This course will introduce you to the linear regression model, which is a powerful tool that researchers can use to measure the relationship between multiple variables. We'll begin by exploring the components of a bivariate regression model, which estimates the relationship between an independent and dependent variable. Building on this foundation, we'll then discuss how to create and interpret a multivariate model, binary dependent variable model and interactive model. We'll also consider how different types of variables, such as categorical and dummy variables, can be appropriately incorporated into a model. Overall, we'll discuss some of the many different ways a regression model can be used for both descriptive and causal inference, as well as the limitations of this analytical tool. By the end of the course, you should be able to interpret and critically evaluate a multivariate regression analysis.
Quantifying Relationships with Regression Models at Coursera Curriculum
Regression Models: What They Are and Why We Need Them
Welcome Video
Correlation
Prediction Error
Introducing the Linear Regression Model
Interpreting Regression Models
Spurious Correlations
Correlation in Statistics
What is a confusion matrix?
Linear Regression and Correlation (Intro & Sections 12.1-12.3)
Correlation Practice Problems
Prediction Error Practice Problems
Linear Regression Practice Problems
Final Quiz on Regression Models: What They Are and Why We Need Them
Fitting and Evaluating a Bivariate Regression Model
Model Fit
Linear Regression Assumptions
Regression with a Binary Treatment Variable
Measures of Fit
The Regression Equation
The Least Squares Assumptions
Dummy Variables
Model Fit Practice Problems
Linear Regression Assumptions Practice Problems
Regression with a Binary Treatment Variable Practice Problems
Final Quiz on Fitting and Evaluating a Bivariate Regression
Multivariate Regression Models
Constructing and Interpreting a Multivariate Model
Dummy Variable Sets
Linear vs. Nonlinear Categorical Variables
Multivariate Model Fit
Introduction to Multivariate Regression Analysis
Interpreting Regression Coefficients
Understanding Dummy Variable Traps in Regression
Adjusted R-Squared: What is it used for?
Multivariate Model Interpretation Practice Problems
Categorical Variable and Dummy Sets Practice Problems
Multivariate Model Fit Practice Problem
Final Assessment on Multivariate Regression
Extensions of the Multivariate Model
Interaction Terms: Introduction
Interacting a Continuous and Dummy Variable
Interacting Two Continuous or Two Dummy Variables
Linear Probability Model
Logit and Probit Models
Interpreting Interactions in Regression
Regression with a Binary Dependent Variable
Interaction Terms: Practice Problems
Binary Dependent Variable Practice Problems
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