Use the dataset named donkeys found on my GitHub repository named data. Perform a short analysis to predict weight based on height using simple linear regression. A short analysis should include at least:

A sentence or two, in your own words (ie not directly copied from the README), explaining what the dataset is all about and what variable you will investigate in your analysis.

A well labeled plot of your variables.

Point estimates of the intercept \(\beta_0\) and slope \(\beta_1\).

Write one complete English sentence explaining each value you just found, in context of the data. Does each estimate make sense? Explain.

What units does the slope coefficient \(\beta_1\) have? Explain when/where/how the units cancel when predicting weight for a donkey with a particular height.

Use the bootstrap method to produce confidence intervals for each value you just found, for a percent confidence of your choice.

Write one complete English sentence describing each confidence interval you just found, in context of the data.

Predict the value of the response variable when the explanatory variable is equal to its median.

Write one complete English sentence describing the value you just found, in context of the data.

Use your bootstrap resampled statistics, without redoing the bootstrap, to produce a confidence interval for the value of the response variable when the explanatory variable is equal to its median.

Write one complete English sentence describing the confidence interval you just found, in context of the data.

Extraploate. Does this prediction make sense, why or why not?

Add to or make a separate well labeled plot that includes a visualization of your analysis.

Calculate adjusted \(R^2\) for this simple linear regression model and interpret it in context of these data.

Using a combination of vectorization and the Pandas DataFrame method loc, remove the one donkey who weighs less than 50 pounds from the dataset. Describe the quantitative and qualitative differences for parts n. through c.