- 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")
head( driving )
library(lsr)
```

- 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

- 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.

- 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

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

- 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

- 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

- use
`correlate`

to test the significance of all pairwise correlations among (numeric) variables in`driving`