## Let's try to predict average hospital infection risk using length
## of stays (days). We'll use the data set
## https://roualdes.us/data/hospital.csv
## Plot the data; be sure to get explanatory and response variables on
## the appropriate axes.
## Fit simple linear regression.
## Interpret the coefficients, intercept and slope, in context.
## Calculate confidence intervals for the intercept and slope and
## interpret both in context.
## Conclude the implicit hypothesis tests for both the slope and the
## intercept using only the confidence intervals above.
## Predict the average infection risk with the average number of stays
## and write a sentence describing this number.
## Interpret the adjusted R^2.
## Try removing the two observations who's stays are longer than 15
## days and refitting the model. Compare models.
## hint: dplyr::filter
## Try plotting both best fit lines, one with all the data and this
## later model with the two observations where stay is greater than 15
## removed, on the same plot -- overlaying all the original data.
## ?geom_abline
## EXTRA
## Calculate a bootstrap confidence interval of the adjusted R^2.