--- title: Regression Analysis description: This statistical method estimates the relationships between dependent and independent variables. Learners will understand how to build, interpret, and evaluate linear and logistic regression models to make predictions and identify trends in data. category: programming-tech subcategory: data-science difficulty: beginner, intermediate, advanced url: /subject/regression-analysis --- # Regression Analysis This statistical method estimates the relationships between dependent and independent variables. Learners will understand how to build, interpret, and evaluate linear and logistic regression models to make predictions and identify trends in data. ## Available Resources 2 Books • 4 Courses • 5 Websites ## Websites ### 1. 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. **Difficulty:** Beginner | **Price:** Free **Link:** https://seeing-theory.brown.edu/basic-probability/index.html **Tags:** probability, statistics, data-visualization, regression, bayesian-inference ### 2. 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. **Difficulty:** Intermediate | **Price:** Free **Link:** https://distill.pub/ **Tags:** deep-learning, neural-networks, interpretability, interactive-visualization, machine-learning ### 3. statsmodels.org 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. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://statsmodels.org **Tags:** websites, mathematics-statistics, statistics-and-probability ### 4. statisticshowto.com Statistics How To is a practical statistics learning site that explains core concepts with clear, step-by-step tutorials and worked examples. It covers hypothesis testing (including p-values and test types) along with a wide range of other stats topics and handy calculators. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://statisticshowto.com **Tags:** websites, mathematics-statistics, statistics-and-probability ### 5. r-statistics.co R-Statistics.co is a practical, example-driven guide to learning statistics with the R programming language. It provides tutorials, explanations, and ready-to-use R code across topics from descriptive statistics to regression, hypothesis testing, and data visualization. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://r-statistics.co **Tags:** websites, technology-computer-science, data-science ## Courses ### 1. Linear Regression and Logistic Regression in Python Master linear & logistic regression in Python with Jose Portilla! Learn key modeling techniques for data analysis. **Difficulty:** Beginner | **Price:** Paid **Link:** https://www.udemy.com/course/linear-regression-in-python/ **Tags:** linear-regression, logistic-regression, python, machine-learning, data-analysis ### 2. 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. **Difficulty:** Intermediate | **Price:** Free **Link:** https://online.stat.psu.edu/stat462/ **Tags:** regression-analysis, linear-regression, logistic-regression, model-diagnostics, statistics ### 3. 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. **Difficulty:** Intermediate | **Price:** Free **Link:** https://www.edx.org/learn/statistics/stanford-university-statistical-learning **Tags:** statistical-learning, regression, classification, r, machine-learning ### 4. 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. **Difficulty:** Beginner | **Language:** English | **Price:** Free **Link:** https://www.coursera.org/learn/regression-models **Tags:** courses, technology-computer-science, data-science--ai ## Books ### 1. Applied Linear Statistical Models **Author:** Michael H. Kutner, Christopher J. Nachtsheim, John Neter, William Li A graduate-level treatment of linear regression, ANOVA, and experimental design, working from simple regression through model diagnostics, variable selection, and factorial experiments. Readers finish able to specify, fit, and critique linear models on real data. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/025608338X?tag=edmonddante07-20 **Tags:** books, technology-computer-science, statistics-data-science ### 2. Regression Modeling Strategies **Author:** Frank E. Harrell Jr. Harrell's reference on building regression models that hold up: spline-based nonlinear terms, handling missing data by imputation, bootstrap validation, and calibration. Aimed at analysts who already fit models and need principled strategy for specification and validation. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/0387952322?tag=edmonddante07-20 **Tags:** books, mathematics-statistics, statistics-and-probability --- *This content is part of Dantes.io - Your Treasure Map to Knowledge* *Curated by humans at Dantes.io. Personal study use welcome; republishing this curation requires permission (team@dantes.io).* View this page online: https://dantes.io/subject/regression-analysis