Pick a dataset (CSV file) from my GitHub repository named data. Most files have READMEs as .txt files

^{1}. Perform a short analysis on two numerical variable of your choice 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. Be sure to be explicit about what the

**observations**are. Decide and justify which numerical variable should be the**explanatory**and which the**response**variable.A well labeled, units and all, plot of your variable. Put axis labels on your plot by using

`bp.labels(...)`

.Point estimates of the intercept \(\beta_0\) and slope \(\beta_1\). Use Scipy’s function

`minimize(...)`

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

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 mean.

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 mean.

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

**Extraploate**, that is make a prediction outside of the range of your data. 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.

If there isn’t an associated README consider helping me out by writing one and filing a PR.↩