John Hopkins University - Regression Models
- Offered byCoursera
Regression Models at Coursera Overview
Duration | 4 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Regression Models at Coursera Highlights
- Earn a Certificate of completion from Johns Hopkins University on successful course completion
- Instructors - Roger D. Peng, Jeff Leek, and Brian Caffo
- Shareable Certificates
- Self-Paced Learning Option
Regression Models at Coursera Course details
- The course is desigend for those who want to learn about regression models and linear models.
- 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.
Regression Models at Coursera Curriculum
Week 1: Least Squares and Linear Regression - This week, we focus on 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
Week 2: Linear Regression & Multivariable Regression - This week, we will work through the remainder of linear regression and then turn to the first part of 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
Week 3: Multivariable Regression, Residuals, & Diagnostics - This week, we'll build on last week's introduction to multivariable regression with some examples and then cover residuals, diagnostics, variance inflation, and model comparison.
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
Week 4: Logistic Regression and Poisson Regression - This week, we will work on generalized linear models, including binary outcomes 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
Regression Models at Coursera Admission Process
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