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Regression Models
Coursera

Summary

Price
£29 inc VAT
Study method
Online
Duration
4 weeks · Self-paced
Qualification
Data Science Specialization; Verified Certificate
Awarded by Coursera/ Johns Hopkins University

Overview

Part of the Data Science Specialization

Learn how to use regression models, the most important statistical analysis tool in the data scientist's toolkit. This is the seventh course in the Johns Hopkins Data Science Specialization.

Description

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.

Course Syllabus


In this course students will learn how to fit regression models, how to interpret coefficients, how to investigate residuals and variability. Students will further learn special cases of regression models including use of dummy variables and multivariable adjustment. Extensions to generalized linear models, especially considering Poisson and logistic regression will be reviewed.

Course Format

Weekly lecture videos and quizzes and a final peer-assessed project.


FAQ


Will I get a Statement of Accomplishment after completing this class?
Yes. Students who successfully complete the class will receive a Statement of Accomplishment signed by the instructor.

What resources will I need for this class?
Students must have the latest version of R and RStudio installed.

How does this course fit into the Data Science Specialization?
This is the seventh course in the sequence. Although it isn't a requirement, we recommend that you first take The Data Scientist's Toolbox and R Programming.

Requirements

Recommended Background

R programming, mathematical aptitude. The content in the  R Programming and Statistical Inference courses covers the necessary background. The material from Statistical inference could be taken concurrently with this class.

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Legal information

This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

FAQs

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