Course Summary
This course teaches regression analysis, a statistical method used for modeling relationships between two or more variables. You will learn how to use regression models to make predictions and analyze data.Key Learning Points
- Understand the basics of regression analysis and learn how to use it to analyze data
- Learn how to interpret regression models and make predictions
- Practice using regression models with real-world examples
Related Topics for further study
Learning Outcomes
- Understand the fundamentals of regression analysis
- Be able to interpret regression models and make predictions
- Practice applying regression analysis to real-world data
Prerequisites or good to have knowledge before taking this course
- Familiarity with basic statistics
- Knowledge of algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
Similar Courses
- Applied Data Science with Python
- Data Science Essentials
- Machine Learning
Related Education Paths
Notable People in This Field
- Andrew Ng
- Nate Silver
- Hilary Mason
Related Books
Description
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.
Knowledge
- Use regression analysis, least squares and inference
- Understand ANOVA and ANCOVA model cases
- Investigate analysis of residuals and variability
- Describe novel uses of regression models such as scatterplot smoothing
Outline
- Week 1: Least Squares and Linear Regression
- Introduction to Regression
- Introduction: Basic Least Squares
- Technical Details (Skip if you'd like)
- Introductory Data Example
- Notation and Background
- Linear Least Squares
- Linear Least Squares Coding Example
- Technical Details (Skip if you'd like)
- Regression to the Mean
- Welcome to Regression Models
- Book: Regression Models for Data Science in R
- Syllabus
- Pre-Course Survey
- Data Science Specialization Community Site
- Where to get more advanced material
- Regression
- Technical details
- Least squares
- Regression to the mean
- Practical R Exercises in swirl Part 1
- Quiz 1
- Week 2: Linear Regression & Multivariable Regression
- Statistical Linear Regression Models
- Interpreting Coefficients
- Linear Regression for Prediction
- Residuals
- Residuals, Coding Example
- Residual Variance
- Inference in Regression
- Coding Example
- Prediction
- Really, really quick intro to knitr
- *Statistical* linear regression models
- Residuals
- Inference in regression
- Looking ahead to the project
- Practical R Exercises in swirl Part 2
- Quiz 2
- Week 3: Multivariable Regression, Residuals, & Diagnostics
- Multivariable Regression part I
- Multivariable Regression part II
- Multivariable Regression Continued
- Multivariable Regression Examples part I
- Multivariable Regression Examples part II
- Multivariable Regression Examples part III
- Multivariable Regression Examples part IV
- Adjustment Examples
- Residuals and Diagnostics part I
- Residuals and Diagnostics part II
- Residuals and Diagnostics part III
- Model Selection part I
- Model Selection part II
- Model Selection part III
- Multivariable regression
- Adjustment
- Residuals
- Model selection
- Practical R Exercises in swirl Part 3
- Quiz 3
- (OPTIONAL) Data analysis practice with immediate feedback (NEW! 10/18/2017)
- Week 4: Logistic Regression and Poisson Regression
- GLMs
- Logistic Regression part I
- Logistic Regression part II
- Logistic Regression part III
- Poisson Regression part I
- Poisson Regression part II
- Hodgepodge
- GLMs
- Logistic regression
- Count Data
- Mishmash
- Practical R Exercises in swirl Part 4
- Post-Course Survey
- Quiz 4
Summary of User Reviews
Discover the art of regression models in this comprehensive online course on Coursera. Students have given high praise for this course, citing its practical approach to teaching and real-world examples.Key Aspect Users Liked About This Course
The course offers a practical approach to teaching and real-world examples that help students understand the subject matter more effectively.Pros from User Reviews
- The course provides a comprehensive overview of regression models and their applications.
- The instructors are knowledgeable and engaging, making the course enjoyable to follow.
- The course is well-structured and easy to navigate.
- The assignments and quizzes are challenging but rewarding.
Cons from User Reviews
- The course can be quite technical and difficult for beginners.
- Some students have experienced technical issues with the platform and course materials.
- The course may not be suitable for individuals who prefer a more theoretical approach to learning.
- The course requires a significant time commitment, which may be challenging for some students.
- The course does not offer much interaction with other students or the instructors.
Brian Caffo, PhD
4.4 Raiting
Introduction to Artificial Neural Network and Deep Learning
- 4.5
-
![]()
![]()
![]()
![]()
![]()
The Best Machine Learning Techniques for Data Science in Java and Neuroph with Application in Image Recognition Machine learning is an extremely hot area in Artificial Intelligence and Data Science....
Machine Learning Optimization Using Genetic Algorithm
- 4.5
-
![]()
![]()
![]()
![]()
![]()
Learn how to optimize Machine Learning algorithms' performances and apply feature selection using Genetic Algorithm It is referred to as hyperparameter tuning or parameter tuning....
Specialized Models: Time Series and Survival Analysis
- 4.5
-
![]()
![]()
![]()
![]()
![]()
This course introduces you to additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. Identify common modeling challenges with time series data...
Foundations of marketing analytics
- 4.6
-
![]()
![]()
![]()
![]()
![]()
Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts....
