An Introduction to Statistical Learning
by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani · James et al.
Accessible introduction to statistical learning covering linear regression, classification, resampling, regularization, trees, and support vector machines, with R labs. Readers finish able to fit and evaluate standard predictive models without heavy theory.
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More resources on Regression & Modeling
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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 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.
STAT 462: Applied Regression Analysis (Penn State)
Penn State's open course notes for applied regression analysis, covering simple and multiple linear regression, model diagnostics, transformations, categorical predictors, model building, and logistic and Poisson regression, with R and Minitab help. Learners can fit, check and interpret regression models on real data.
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StatQuest: Linear Regression Clearly Explained
Josh Starmer walks through fitting a line by least squares, then explains R-squared and the p-value for the fit using simple visuals. Good for building intuition before meeting the algebra of regression.
Statistical Learning
Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in R. Course material updated in 2021 for second edition of the course textbook.