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What you'll learn

Describe the assumptions of the linear regression models.

Use R to fit a linear regression model to a given data set.

Interpret and draw conclusions on the linear regression model.

Skills you'll gain

Probability & Statistics

Regression Analysis

Linear Algebra

Predictive Modeling

Statistical Inference

R Programming

Data Analysis

Statistical Analysis

There are 4 modules in this course

This course is part of the Performance Based Admission courses for the Data Science program. This course will focus on getting you acquainted with the basic ideas behind regression, it provides you with an overview of the basic techniques in regression such as simple and multiple linear regression, and the use of categorical variables. Software Requirements: R Upon successful completion of this course, you will be able to: - Describe the assumptions of the linear regression models. - Compute the least squares estimators using R. - Describe the properties of the least squares estimators. - Use R to fit a linear regression model to a given data set. - Interpret and draw conclusions on the linear regression model. - Use R to perform statistical inference based on the regression models.

Module 2: Multiple Linear Regression

Module 3: Regression Models with Qualitative Predictors

Summative Course Assessment