Regression Analysis in R for Data Science: from Zero to Hero

Install R and R Studio on your computer, customize the R interface, and quickly start using R.

Discover R and RStudio as open-source, cross-platform tools for statistical computing and graphics in data science, with GUI options, package extensibility, and reproducible research through embedded scripts.

Install R and RStudio by downloading from CRAN and the RStudio site, selecting your operating system, then unzipping and installing. The free version meets most needs.

Install R and RStudio on Windows by downloading from CRAN and the RStudio site, then run the installers with default settings. Explore manuals, FAQs, and community forums for help.

Explore the RStudio interface essentials, including the source, console, environment, and file panes; set up and manage working directories, execute code efficiently, and access built-in help and shortcuts.

This lab introduces the RStudio interface with its four panes, shows how to set up a working directory, use get help, and run scripts to produce console output and plots.

Explore the essentials of R scripting, including installing and managing packages, basic data types and structures (vectors, factors, data frames), functions, and reading data into R.

Learn to install and manage packages in RStudio, using the Tools menu or install.packages, load with library, and call specific functions like bedfast and Jaja Plot.

Master defining and using variables in R with the assign operator, view them in the environment pane, call and compute with variables, and follow naming conventions using points.

assign variable x as the sum of eight and seven. assign variable agric as the product of four and two and print the results in the console or environment pane.

Explore data types and data structures in R, including vectors, atomic types, and factors, and learn how to inspect classes and attributes for objects.

This lab demonstrates assigning and coercing variable types in R, including numeric, integer, character, and logical, and working with factors and ordered levels for map legends.

Learn about six vector types in R, declare and combine vectors, understand type coercion to character, index and recycle, and use built-in functions like max and var for data science.

Factors represent categorical data and aid analysis and data input. Strings convert to factors by default, and integer values map to levels ordered alphabetically; adjust level order with levels argument.

Explore data frames as the primary two-dimensional data type in R, with variables like name, size, and weight, and use structure and summary to inspect types and observations.

Explore for loops in R, an overview of iterating over vectors, using length to set iterations, indexing elements, and printing results.

Learn how to use for loops in R to iterate over integers and vectors, print results, and write generic, element-wise operations for data science tasks.

Learn to import data into R from csv, text, and excel formats by setting the working directory, reading with read.table, read.csv, and read_excel, and previewing and summarizing the data.

Explore regression analysis, from simple to multiple, including principal components regression, cross-validation, and predicting a continuous dependent variable from independent variables.

Use graphical analysis to prepare regression in R, employing scatter plots, density plots, and box plots to reveal linear relationships, outliers, and normality before fitting a linear model.

Master your first linear regression in R with the diamonds data, using carat–price scatterplots, outlier checks, normality assessment, a full model, and price prediction.

Explore how to assess linear dependence between variables using correlation in R, focusing on diamond carat and price with a coefficient near 0.924, indicating a positive relationship.

Assess model fit using R-squared, adjusted R-squared, F statistics, and p-values, and compare AIC and Bayesian information criterion to select the best model.

Construct a simple linear regression model to predict diamond price from carat, then perform diagnostics on significance, coefficients, and fit using r-squared and adjusted r-squared on the diamond data.

Explore how AIC and BIC—the Akaike and Bayesian information criteria—measure goodness of fit and guide model selection in regression, comparing multiple models with the same data, where lower is better.

Evaluate prediction model performance in supervised learning using regression and RMSE, compare classification accuracy with confusion matrix metrics, and distinguish in-sample versus out-of-sample error to assess generalization.

Learn to predict diamond prices using a full linear regression model with all variables, and assess in-sample fit via RMSE from the predict function.

Split the data 80/20 with a reproducible seed to train on 80% and evaluate on the remaining 20%. Compute out-of-sample rmse to assess model performance.

Estimate a multiple linear regression model to predict sales from YouTube, Facebook, and newspaper spending in R. Evaluate coefficients, p-values, and R-squared, then refine by removing insignificant predictors.

Predict sales using a multiple linear regression model with Facebook, YouTube, and newspaper advertising, and evaluate predictions with mean square error.

Learn how to model sales with multiple linear regression including interaction effects between YouTube and Facebook advertising, interpret main and interaction coefficients, and compare models using R-squared.

Explore dummy coding of categorical predictors in regression with R, building a linear model using sex to compare salaries. Understand how the contrast matrix and arbitrary coding affect coefficient interpretation.

Explore how ANOVA extends linear regression to categorical variables with more than two levels in data science using R, dummy coding, and contrast metrics to interpret salary effects.

Explore nonlinear regression essentials in R, including polynomial, spline, and generalized additive models, to capture non-linear relationships and compare models using RMSE and R-squared.

Explore polynomial regression in R to model nonlinear relationships between Boston median house value and lower status population, using 80/20 training/testing, ggplot visualization, and a final fifth-order model for predictions.

Explore how log transformation addresses skewness and improves normality in regression, using a linear model to predict Boston suburb median price from lower-status data, evaluated by R-squared.

Explore spline regression in R as a nonlinear alternative to polynomial regression. Use cubic splines with knots on the Boston data to predict median house value from lower-status population.

Explore generalized additive models in R to automatically fit spline-based non-linear effects, blending linear and non-linear terms, and evaluate model performance on the Boston housing data.

Explore decision trees (CART) for classification and regression, showing how splits like diameter partition data into leaves, and how pruning and bootstrap aggregation prevent overfitting in random forests.

Learn to classify data with decision trees in R using the airport package on sonar data, including model visualization, prediction, and evaluating accuracy and sensitivity.

Explains random forest as an ensemble of classification and regression trees, using two-thirds of training data and grid search to tune the split-variable parameter in R.

Fit a random forest in Ranger to classify mines from rocks using high-dimensional sonar data, with a 60/40 train-test split and model tuning.

Compare predictive models using the caret framework on a churn dataset from the C50 package, applying five-fold cross-validation to identify the best model, with random forest leading.

Discover how to select predictors in R to build parsimonious, interpretable models without sacrificing performance, using best subset regression, stepwise regression, penalized regression, and principal components regression methods.

Complete your final project by testing five distinct models on the model comparison lab data. Compare performance and select the best model; optionally repeat with your own data.

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