Regression Models
Coursera
Course Summary
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.
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Course 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.
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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.
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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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Course Format
Weekly lecture videos and quizzes and a final peer-assessed project.
