Link to Content:
Johns Hopkins University
Roger D. Peng
Content Found Via:
Free? Partially: Some Free Content, Some Paid
Tags: ANOVA / least squares / linear regression / logistic regression / multivariable regression / regression / statistics
This is course 7 of 10 in the Coursera Data Science Specialization.
“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.
- Week 1: Least Squares and Linear Regression
- Week 2: Linear Regression & Multivariable Regression
- Week 3: Multivariable Regression, Residuals, & Diagnostics
- Week 4: Logistic Regression and Poisson Regression”
Recommended Prerequisites: Some programming experience
Go to Content: Regression Models