--- title: Regression & Modeling description: Regression analysis is a statistical method for estimating relationships between variables. Learners will understand how to build, evaluate, and interpret linear and logistic regression models to predict outcomes and analyze data trends. category: programming-tech subcategory: data-science difficulty: beginner, intermediate, advanced url: /subject/regression-and-modeling --- # Regression & Modeling Regression analysis is a statistical method for estimating relationships between variables. Learners will understand how to build, evaluate, and interpret linear and logistic regression models to predict outcomes and analyze data trends. ## Available Resources 1 Videos • 2 Books • 4 Courses • 3 Websites ## Courses ### 1. 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 ### 2. 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 ### 3. Machine Learning In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.coursera.org/learn/machine-learning **Tags:** supervised-learning, linear-regression, logistic-regression, python, scikit-learn ### 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 ## Websites ### 1. Scikit-learn User Guide: Linear Models Reference section of the scikit-learn documentation covering ordinary least squares, ridge, lasso, elastic net, and logistic regression, with the API calls and short code examples needed to fit and regularize these models in Python. **Difficulty:** Beginner | **Price:** Free **Link:** https://scikit-learn.org/stable/modules/linear_model.html **Tags:** scikit-learn, linear-regression, regularization, python, machine-learning ### 2. The Elements of Statistical Learning Companion site for Hastie, Tibshirani, and Friedman's graduate text, offering the full book as a free PDF plus datasets and errata. Covers regression, classification, trees, boosting, and unsupervised methods at a mathematically demanding level. **Difficulty:** Beginner | **Price:** Free **Link:** https://hastie.su.domains/ElemStatLearn/ **Tags:** statistical-learning, regression, classification, machine-learning, free-textbook ### 3. Statistical Modeling, Causal Inference, and Social Science **Author:** Andrew Gelman Long-running blog by Columbia statistician Andrew Gelman and co-authors, discussing Bayesian modeling, multilevel regression, causal inference, and flawed published research. Regular reading builds judgment about model checking, statistical significance, and how applied analyses go wrong in practice. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://statmodeling.stat.columbia.edu **Tags:** statistical-modeling, bayesian-statistics, causal-inference, multilevel-models, research-methods ## Videos ### 1. 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. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.youtube.com/watch?v=nk2CQITm_eo **Tags:** linear-regression, r-squared, least-squares, statistics, statquest ## Books ### 1. An Introduction to Statistical Learning **Author:** Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani 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. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/1461471370?tag=edmonddante07-20 **Tags:** books, technology-computer-science, data-science--ai ### 2. Applied Regression Analysis **Author:** Norman R. Draper, Harry Smith Classic reference on least squares regression, covering model fitting, residual analysis, variable selection, multicollinearity, and nonlinear models. Written for practitioners who need the underlying matrix algebra and diagnostics rather than software recipes. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/0471029955?tag=edmonddante07-20 **Tags:** books, technology-computer-science, data-science--ai --- *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-and-modeling