- load the
`driving.Rdata`

file. - type
`head( driving )`

to look at the first few observations - load the following packages:
`lsr`

,`car`

```
load( "~/Work/Research/Rbook/workshop_dsto/datasets/driving.Rdata")
head( driving )
library(lsr)
library(car)
```

- use
`lm()`

to fit a regression model with the RT at time 1 as the outcome variable, including age and errors at time 1 as predictors. Save the results to a variable called`mod.1`

- use
`summary()`

to run the hypothesis tests etc associated with`mod.1`

- use
`standardCoefs()`

to extract standardised regression coefficients

- use
`plot()`

to draw the standard regression diagnostic plots associated with`mod.1`

- use
`aov()`

to fit an ANOVA model with number of errors at time 1 as the outcome, and with groups defined by distractor type. Save the results to a variable called`mod.2`

. - use the
`Anova()`

function [not`anova()`

] to produce the ANOVA table - use
`etaSquared()`

to estimate the effect size - use
`TukeyHSD()`

to run posthoc tests - also, use
`bars()`

to plot the group means and confidence intervals, since that’s always useful when making sense of the results!

- use
`aov()`

to fit an ANOVA model that has number of errors at time 1 as the outcome, and with groups defined by distractor type and by peak hour. Include interaction terms in the model. Save the results to a variable called`mod.3`

. - use
`Anova()`

to produce the ANOVA table - use
`etaSquared()`

to estimate the effect size - use
`TukeyHSD()`

to run posthoc tests - also, use
`bars()`

to plot the group means and confidence intervals, since that’s always useful when making sense of the results!

- use
`lm()`

to fit a stage one model that has errors at time 1 as the outcome, and age and gender as predictors (save it as`mod.4a`

). Then fit the stage 2 model that also includes distractor as an additional predictor (`mod.4b`

). - use
`anova()`

to run the hierarchical regression