# Software Resources for R

Below is a list of resource pages for using R to do statistics. On each page a set of data are explored with the software. Some commentary is given on interpretation but the main focus is on getting the software to do the work. What the numbers mean should come from your prior statistical training or the statistics.com course you are currently taking. Each page listed below has a title you can click on to read the page. Beneath the title is a list of topics covered on that page.

## Getting Started

• Entering data
• Simple summary statistics
• Stem and leaf plots
• Histograms
• Boxplots
• Dotplots
• Logarithmic transformations

## Summarizing Quantitative Data

• Simple summary statistics
• Boxplots comparing two groups
• Transformations

• Mode
• Tables
• Bar charts
• Pie charts

## Making and Interpreting Tables for Two Categorical Variables

• One- and Two-Way Tables
• Probability and two-way tables

## Inference for One Proportion

• Confidence interval
• Hypothesis test

## Inference for Two Proportions

• Confidence interval
• Hypothesis test

## Chi-Squared Tests

• One-way (Goodness of Fit)
• Two-way (Contingency Tables)

## Inference for a Single Mean

• Confidence interval
• Hypothesis test

## Inference for Two Means (Independent Samples)

• Confidence interval
• Hypothesis test

## Inference for Paired Differences

• Confidence interval
• Hypothesis test

## Transformations in R

• Straightening a curved relationship by transforming a variable

## Simple Linear Regression

• Simple plots for each variable
• Scatterplots
• Correlation and covariance
• Running a regression
• Scatterplot with regression line
• Inference for regression coefficients
• Regression through the origin
• Scatterplot with multiple models
• Making predictions

## Regression with Two Independent Variables

• Reading data from a text file
• Correlations
• Regression with one or two independent variables
• Brief discussion of multicollinearity and autocorrelation
• Basic residual plots

## One-way Analysis of Variance

• Reading text data files into R
• Stacking data
• Boxplots comparing multiple groups
• ANOVA

## Two-way Analysis of Variance

• Finding subgroup means