# Exercises for 2.3: Simple inferential statistics

## Preliminaries

• load the driving.Rdata file.
• type head( driving ) to look at the first few observations
• load the following packages: lsr
load( "~/Work/Research/Rbook/workshop_dsto/datasets/driving.Rdata")
library(lsr)

## Exercise 2.3.1: Confidence intervals

• use ciMean() to calculate the 95% confidence interval for the mean age
• use ciMean() to calculate the 99% confidence interval for the mean age
• use ciMean() to calculate 95% confidence intervals for all variables
• use aggregate and ciMean to calculate 95% confidence intervals for the mean age separately for each distractor type

## Exercise 2.3.2: Independent samples t-test

• Run a t-test comparing the number of errors (at time 1) for the peak hour group versus the non peak hour group.
• Calculate Cohen’s d.

## Exercise 2.3.3: Paired samples t-test

• Run a paired samples t-test to see if the number of errors made at time 2 differs from the number of erros people made at tie 1

## Exercise 2.3.4: Chi-square goodness of fit tests

• Run a chi-square goodness of fit to see if the number of females vs males differs significantly from chance

## Exercise 2.3.5: Chi-square tests of association

• Run a chi-square test of association to see if there is a significant association between gender and distractor type
• Calculate Cramer’s V for the association

## Exercise 2.3.6: Testing the significance of a single correlation

• use cor.test to see if the Pearson correlation between errors and RT at time 1 is significant
• again using cor.test, see if the Spearman correlation between errors and RT at time 1 is significant

## Exercise 2.3.7: Testing all pairwise correlations

• use correlate to test the significance of all pairwise correlations among (numeric) variables in driving