**Page 3**- 1.3 Bringing data into R from an Excel file or a text file
- 1.3.1 Bringing data into R from an Excel file using the read.csv(file.choose()) command
- 1.3.2 (Optional) Bringing data into R from an Excel file using the read.csv() command
- 1.3.3. Accessing individual variables from an imported data set
- The 'dataframename$variablename' convention
- The attach( ) command
- 1.3.4 Viewing or editing a data frame using the R data editor
- 1.3.5 (Optional) Bringing data into R from a space-delimited text file
- 1.3.6 (Optional) Specifying the default folder for R

**Page 4**- 1.4 Creating new variables in R
- 1.4.1 Calculating new variables
- 1.4.2 Creating categorical variables

**Page 6**- 1.7 Finding means, medians and standard deviations
- 1.8 Finding frequencies and proportions for categorical variables

**Page 10**- 1.12 Statistical tables in R
- The standard normal (z) distribution
- The t distribution
- The chi-square distribution

**Page 11**- 2.1 Confidence Intervals for a Single Group
- 2.1.1 Confidence interval for a mean
- 2.1.2 Confidence interval for a proportion
- Confidence Intervals for Comparing Means
- 2.1.3 Confidence interval for a difference in means, independent samples
- 2.1.4 Confidence interval for a mean difference, paired samples
- Confidence Intervals for Comparing Frequencies
- 2.1.5 Confidence interval for the difference in proportions, independent samples
- 2.1.6 Confidence interval for a risk ratio
- 2.1.6.1 Confidence interval for a RR from a per-subject data set
- 2.1.6.2 Inputting counts from a 2x2 table into R for calculation of a RR
- 2.1.7 Confidence interval for an odds ratio

**Page 12**- 2.2 t-tests for means of measurement outcomes
- 2.2.1 The one-sample t-test for a mean
- 2.2.2 The independent samples t-test to compare two means
- 2.2.3 The paired samples t-test

**Page 13**- 2.3 z-tests for proportions, categorical outcomes
- 2.3.1 One-sample z-test for a proportion
- 2.3.2 Two-sample z-test comparing two proportions

**Page 15**- 2.5 Chi-square tests for categorical outcoomes
- 2.5.1 The chi-square goodness-of-fit test for one sample
- 2.5.2 Contingency table analysis and the chi-square test of independence
- 2.5.2.1 The chi-square test of independence from per-subject data
- 2.5.2.2 The chi-square test of independence from tabled data
- 2.5.2.3 Fisher's exact test for small cell sizes
- 2.5.2.4 Relative Risk and Confidence interval for the RR
- 2.5.2.5 Odds ratios and 95% CI for the OR

**Page 16**- 2.6 Nonparametric statistics for comparing medians of non-normal outcomes
- 2.6.1 Wilcoxon rank sum test for independent samples
- 2.6.2 Wilcoxon signed rank test for paired samples

**Page 17**- Section 3: Power and sample size calculations
- 3.1 Comparing means between groups
- 3.2 Comparing proportions between groups

**Page 18**- Section 4: Association between variables and multivariable methods to control for confounding
- 4.1 Simple correlation and regression
- 4.1.1 Scatterplots
- 4.1.2 Correlation
- 4.1.3 Simple regression analysis
- 4.1.4 Spearman's nonparametric correlation coefficient

**Page 19**- 4.2 Multiple linear regression for a measurement outcome
- 4.2.1 Multiple regression analysis
- 4.2.2 Multiple regression with categorical predictors
- 4.2.3 Finding standardized regression coefficients in R