Recent articles



Linear Regression Essentials in R




Linear regression (or linear model) is used to predict a quantitative outcome variable (y) on the basis of one or mult...

Interaction Effect in Multiple Regression: Essentials




This chapter describes how to compute multiple linear regression with interaction effects. Previously, we have describ...

Regression with Categorical Variables: Dummy Coding Essentials in R




This chapter describes how to compute regression with categorical variables. Categorical variables (also known as fact...

Nonlinear Regression Essentials in R: Polynomial and Spline Regression Models




In some cases, the true relationship between the outcome and a predictor variable might not be linear. There are diffe...

Linear Regression Assumptions and Diagnostics in R: Essentials




Linear regression (Chapter @ref(linear-regression)) makes several assumptions about the data at hand. This chapter des...

Multicollinearity Essentials and VIF in R




In multiple regression (Chapter @ref(linear-regression)), two or more predictor variables might be correlated with eac...

Confounding Variable Essentials




A Confounding variable is an important variable that should be included in the predictive model but you omit it.Naive i...

Regression Model Accuracy Metrics: R-square, AIC, BIC, Cp and more




In this chapter we’ll describe different statistical regression metrics for measuring the performance of a regressio...

Cross-Validation Essentials in R




Cross-validation refers to a set of methods for measuring the performance of a given predictive model on new test dat...

Bootstrap Resampling Essentials in R




Similarly to cross-validation techniques (Chapter @ref(cross-validation)), the bootstrap resampling method can be used...

Best Subsets Regression Essentials in R




The best subsets regression is a model selection approach that consists of testing all possible combination of the pre...

Stepwise Regression Essentials in R




The stepwise regression (or stepwise selection) consists of iteratively adding and removing predictors, in the predict...

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Books on data science




Machine Learning Essentials




Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This bo...[Read more]

R Graphics Essentials for Great Data Visualization: 200 Practical Examples You Want to Know for Data Science




This book provides more than 200 practical examples to create the right graphics for the right data using either the ggplot2 pac...[Read more]

Practical Guide to Principal Component Methods in R




This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large mu...[Read more]

R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics




With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently....[Read more]

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