(a) This data has been loaded in the `studyhours` dataframe (with variables `class` (Freshmen or Sophomores) and `hours` (spent studying per week).

(b) Conduct a randomization-based test to see if the data support my hypothesis. Write out your null hypothesis, the value of your test statistic, and the estimated p-value. Plot the distribution of randomized variance ratios.

(c) Use bootstrap methods to construct an 88% confidence interval for the ratio of variances.

(d) Use the `var.test` function to conduct an F-test to compare group variances. Make sure you evaluate the assumption(s) necessary to conduct this test.

(e) Based on all of this, what are you willing to conclude regarding the variances in hours studying for first-year and sophomore students? ### (a) ```{r 'part-a'} # This will load the dataframe studyhours <- read.csv("http://www.bradthiessen.com/html5/data/studyhours.csv") head(studyhours) ```

### (b) ```{r 'part-b'} # Conduct the randomization-based test # You can write your null hypothesis as a comment: # Null hypothesis: # Calculate your test statistic # Estimate the p-value # Plot the randomized distribution ```

### (c) ```{r 'part-c'} # Construct the bootstrap confidence interval # Note you're trying to get an 88% interval ```

### (d) ```{r 'part-d'} # Conduct the variance test # Check assumptions ```

### (e) **Write your conclusions here**

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You're done!