1. ...and it went straight to my cerebellum.
Below is a list of the number of beers a student had and the BAC they had 30 minutes later. (from Yates, Starnes, Moore, 3d ed. p. 177)
Beers=c(5,2,9,8,3,7,3,5,3,5,4,6,5,7,1,4)
BAC=c(0.1, 0.03, 0.19, 0.12, 0.04, 0.095, 0.07, 0.06, 0.02, 0.05, 0.07, 0.1, 0.085, 0.09, 0.01, 0.05)
a) create an appropriate plot for this data.
b) create a line of best fit for this data.
c) How is the correlation?
c) If a student has 10 beers, what do we expect the BAC to be?
d) A student has a BAC of 0.18. How many beers do we expect they have consumed?
e) Can we use this to explain what the BAC of a student will be after they have had 30 beers? Why or why not?
2. Data imported from Detroit
Below is a data set dealing with statistics from Detroit. The details can be found here.
load the csv file into r. Save the data as detroit
use: plot(detroit) . Explain what r gave you in this graph. If you ae unsure, call me over and we will look at it together.
Find two variables you think would be described well with a line of best fit, either by looking at the names or at the graph your created.
find the correlation between your two variables,
find the line of best fit,
and then create a plot that includes the points, correct axes labels, and the line of best fit.
Explain what relationship you found and what it means in real life.
3. Grand Grads
Here is a list of the number of graduate students per 1000 people for each of the fifty states:
grads=c(37,41,41,41,41,41,43,43,45,45,45,45,46,46,46,47,47,47,47,47,48,48,48,48,49,50,50,51,51,51,52,52,52,54,54,54,54,55,56,56,60,60,60,60, 61,67,69,71,72,77)
Are there any outliers in this data? Use your knowledge and facts from the data to decide. If you do decide there is an outlier that should be removed, do so before continuing the rest of these questions.
Make a graph for this data. Make it look nice in r (as nice as you can).
What is the z-score for the state with 61 students per 1000?
A new state is discovered [weird, I know] that has more grad students per thousand than 89.97% of the other states. About how many grad students would you expect per 1,000?
4. What do you call a couple that's been together for 60 years?
Below is a data set for Old Faithful eruptions. The first data column is the duration of the eruption (in seconds). The second column is the Interval of Time before the next eruption (in minutes). We are going to use duration as the explanatory and use interval as the response variable.
Get a correlation between our data, find a line of best fit, and plot your data (and line).
Does a linear relationship make sense for this data? Explain any possible problems with using a linear relationship with this data.
If an eruption were to last for 256 seconds, what would the expected Interval time be?
If an Interval was 88 minutes, what would we expect the eruption time to be?
One of the points given was (207, 78). What is the residual of this point?
5. COME AND GET YOUR SECRET DATA.
Get the following information about your data:
x mean,
y mean,
x std. dev,
y st. dev,
correlation,
and equation for line of best fit.
plot your data and your line of best fit on a graph in r. do not show this graph to any other group.