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 will cover regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well in this.
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
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Course Credit: JHSPH Open
|Lecture 1.1 Introduction to Regression
|Lecture 1.2 Basic Notation and Background
|Lecture 1.3 Linear Least Squares
|Lecture 1.4 Regression to the Mean
|Lecture 1.5 Statistical Linear Regression Models
|Lecture 1.6 Residuals
|Lecture 1.7 Inference in Regression
|Lecture 2.1 Multivariate Regression
|Lecture 2.2 Multivariable Regression Example
|Lecture 2.3 Multivariable Simulation Exercises
|Lecture 2.4 Residuals
|Lecture 2.5 Some thoughts on model selection
|Lecture 3.1 Generalized Linear Models
|Lecture 3.2 Binary Data GLMs
|Lecture 3.3 Poisson Regression
|Lecture 3.4 Fitting Functions
|Submit Your Assignment
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