Quantifying Relationships with Regression Models
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Course Summary
This course explores the use of regression models to quantify relationships between variables. It covers linear, multiple, and logistic regression, as well as model selection and validation.Key Learning Points
- Learn how to use regression models to analyze data
- Understand the differences between linear, multiple, and logistic regression models
- Explore model selection and validation techniques
Job Positions & Salaries of people who have taken this course might have
- USA: $62,453
- India: ₹407,591
- Spain: €29,791
- USA: $62,453
- India: ₹407,591
- Spain: €29,791
- USA: $73,207
- India: ₹602,100
- Spain: €35,100
- USA: $62,453
- India: ₹407,591
- Spain: €29,791
- USA: $73,207
- India: ₹602,100
- Spain: €35,100
- USA: $120,931
- India: ₹1,011,847
- Spain: €52,288
Related Topics for further study
Learning Outcomes
- Understand the fundamentals of regression analysis
- Be able to apply regression models to real-world datasets
- Learn how to validate and select models for optimal predictive power
Prerequisites or good to have knowledge before taking this course
- Basic math skills
- Familiarity with statistics
- Access to a computer with a spreadsheet program
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Data Science: Linear Regression in Python
- Applied Data Science: Machine Learning
- Data Science: Logistic Regression
Related Education Paths
Related Books
Description
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.
Outline
- 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
Summary of User Reviews
Learn about quantifying relationships and regression models with this course on Coursera. Students have given it high ratings and found it to be a valuable resource for understanding complex concepts in a simple way.Key Aspect Users Liked About This Course
Many users appreciated the clear and concise explanations provided in the course, making it easy to understand and apply the concepts learned.Pros from User Reviews
- The course is well-structured and easy to follow, even for beginners
- The instructor is knowledgeable and presents the material in an engaging way
- The course provides practical examples and exercises to reinforce learning
Cons from User Reviews
- Some users found the course to be too basic and lacking in depth
- The course may be too focused on theory for some users
- The course does not cover more advanced topics in regression analysis
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