# Problem set 4

due Monday, November 20 at 11:59pm

Estimated time: 6 hours

Allocate about 1 hour per problem, though some will take longer than others. You may need more time if programming is completely new to you, or less if you have some experience already.

Instructions
Upload your .ipynb notebook to gradescope by 11:59pm on the due date.
• Note that each problem will be graded according to this rubric. Solutions that include packages or functions not covered in this course will recieve a score no higher than 2.
• You may collaborate with any of your classmates, but you must write your own code/solutions, understand all parts of the problem, and name your collaborators.
• You should also cite any outside sources you consulted, like Stack Overflow or ChatGPT, with a comment near the relevant lines of code (see example below). Recycled code that has not been cited will be considered plagerism and receive a zero.
# code here was inspired by user2554330 on stack overflow:
# https://stackoverflow.com/questions/69091812/is-everything-a-vector-in-r

## Problem 0

Create a new colab R notebook. Please include the title “Problem set 4”, your name, the date, and any collaborators somewhere at the top.

## Problem 1

Import the data available at

"https://kathrynschuler.com/datasets/model-reliability-cubic.csv"

## Problem 2

Explore the data with (at least) glimpse and a scatterplot. Include a visualization of a simple linear model (y ~ x) using geom_smooth. You may include any other explorations you wish to perform.

## Problem 3

Fit a cubic polynomial model using poly() to the data and store your results as observed_fit. Use whichever of the three methods we learned in class that you prefer. Be sure to return the fitted model so we can see the parameter estimates.

## Problem 4

Estimate the accuracy of the model on the population using bootstrapping or k-fold cross validation (choose one, not both). Use the collect-metrics() function to return the $$R^2$$ value.

## Problem 5

Use infer to get a bootstrapped 68% confidence interval around the parameter estimates of your model. Visualize your bootstrapped distribution and shade the confidence interval.

## Problem 6

Replot your scatterplot of the data and this time plot the cubic polynomial with geom_smooth. Use the level argument to include the 68% confidence interval.