Statistics in R - The R Language for Statistical Analysis

Discover the exercise database at Armani's tutorials dot com, with ten question blocks on functions, data frames, and graphs, and learn how to access and use it for R practice.

Learn to perform a one-sample t test in R to assess a population mean, specify X argument and mu, choose the alternative, and interpret a 95% confidence level and p-values.

Apply the Mann Whitney U test, a nonparametric alternative to the t-test, to compare medians between two independent groups (manual vs automatic) with a 95 percent confidence interval.

Learn Tukey hsd post hoc testing after anova to adjust p-values for pairwise group differences, using iris species with equal group sizes; view confidence intervals and mean differences.

Create a three-level grouping (A, B, C) dataset, visualize with box, violin, and q plots, then run anova and tukey post hoc tests to compare groups.

Explore how outliers influence results and compare three-sigma and box-plot rules using Q1, Q3, and quartile distance, plus model-based and proximity-based multivariate methods for detecting and visualizing anomalies.

Study theory of statistical modeling and predict the outcome variable from predictors using linear regression, intercepts, slopes, and coefficients. Learn about residuals, scatterplots, and simple versus multiple linear regression.

Compare linear and generalized linear models, noting non-normal error terms and non-constant variance, and interpret glm coefficients as multiplicative effects via e^b; discuss polynomial regression and smoothing for nonlinear data.

Explores linear regression on the speed–distance data, showing how speed predicts stopping distance with a strong positive correlation, model coefficients, residual checks, and extrapolation using predict.

Explore multiple linear regression in R by modeling mpg with weight and rear axle ratio, interpret coefficients, assess model fit (R^2 ~ 0.76), and extend with squared terms.

Apply logistic regression on a binary outcome to predict transmission type using a generalized linear model with the binomial family, refining predictors by significance and predicting probabilities.

Examine logistic regression with the plant growth data, using weight to predict group membership. Fit the model, test weight significance, and predict if weight 7.5 belongs to group 2.

Explore bootstrapping and averaging in random forests to boost accuracy, compare them with bagging, and assess performance using a test set.

Explore getting data into Rcmdr: import from files or create datasets with the editor, subset and clean data, and visualize with histograms and scatterplots using preinstalled datasets.