Recent articles
Linear Regression Essentials in R
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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
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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
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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
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In some cases, the true relationship between the outcome and a predictor variable might not be linear. There are diff...
Linear Regression Assumptions and Diagnostics in R: Essentials
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Linear regression (Chapter @ref(linear-regression)) makes several assumptions about the data at hand. This chapter des...
Multicollinearity Essentials and VIF in R
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In multiple regression (Chapter @ref(linear-regression)), two or more predictor variables might be correlated with eac...
Confounding Variable Essentials
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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
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In this chapter we’ll describe different statistical regression metrics for measuring the performance of a regressio...
Cross-Validation Essentials in R
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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
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Similarly to cross-validation techniques (Chapter @ref(cross-validation)), the bootstrap resampling method can be used...
Best Subsets Regression Essentials in R
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The best subsets regression is a model selection approach that consists of testing all possible combination of the pre...
Stepwise Regression Essentials in R
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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
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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
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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
![](.//upload/principal_component_methods_in_r_frontcover_100px.png)
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
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With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently....[Read more]
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