statsmodels.org
Unknown
Statsmodels.org is the online hub for the Statsmodels Python library, offering documentation, API references, tutorials, and examples for statistical modeling and regression analysis (OLS, GLM, mixed-effects) and related tests.
More resources on Regression Analysis
Distill.pub
Peer-reviewed web journal of machine learning explanations, publishing interactive articles on topics like feature visualisation, attention and neural network interpretability. Archive remains readable, though the journal went on indefinite hiatus in 2021 and no longer publishes.
Regression Models
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.
Seeing Theory
An interactive, D3.js-based walkthrough of probability and statistics in six chapters, from basic probability and distributions through frequentist inference, Bayesian inference and regression analysis. Parameters are manipulable, so sampling behaviour and fit are visible directly.
Linear Regression and Logistic Regression in Python
Master linear & logistic regression in Python with Jose Portilla! Learn key modeling techniques for data analysis.
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.
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.