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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


Course Curriculum

Module 01
Lecture 1.1 Introduction to Regression 00:10:00
Lecture 1.2 Basic Notation and Background 00:05:00
Lecture 1.3 Linear Least Squares 00:10:00
Lecture 1.4 Regression to the Mean 00:05:00
Lecture 1.5 Statistical Linear Regression Models 00:10:00
Lecture 1.6 Residuals 00:10:00
Lecture 1.7 Inference in Regression 00:10:00
Module 02
Lecture 2.1 Multivariate Regression 00:15:00
Lecture 2.2 Multivariable Regression Example 00:25:00
Lecture 2.3 Multivariable Simulation Exercises 00:10:00
Lecture 2.4 Residuals 00:10:00
Lecture 2.5 Some thoughts on model selection 00:10:00
Module 03
Lecture 3.1 Generalized Linear Models 00:10:00
Lecture 3.2 Binary Data GLMs 00:10:00
Lecture 3.3 Poisson Regression 00:20:00
Lecture 3.4 Fitting Functions 00:10:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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