## Consider our beloved email dataset.
## 1. Make a plot of the variable line_breaks and describe the
## plot.
## 2. Using the CLT and hence qt(), calculate a 98% confidence
## interval of the sample mean of line_breaks. Interpret your
## conifedence interval in context.
## 3. Calculate a 98% bootstraped confidence interval of the sample mean
## of line_breaks. Interpret your conifedence interval in context.
## Use type "bca".
## 4. Compare your confidence intervals.
## 5. Using the CLT, calculate a 98% confidence interval of the sample
## median of line_breaks.
## Hint: since the CLT doesn't quite help us here (the median is not
## the sample mean), quit and move on to 6.
## 6. Calculate a 98% bootstraped confidence interval of the sample
## median of line_breaks. Interpret your conifedence interval in
## context. Use type "bca".
## 7. Calculate a 95% confidence interval of the difference of sample
## means of number of line breaks by format -- two sample t-test.
## Interpret your confidence interval in context.
## 8. Calculate a 95% bootstraped confidence interval of the difference
## of sample means of number of line breaks by format -- bootstrap
## equivalent two sample t-test. Interpret your confidence interval
## in context.
## 9. Compare your intervals.