For this project you will be developing a regression model based on car crash data that is already in seaborn. You can import it using the following code:

import numpy as np

import pandas as pd

import seaborn as sns

df $=$ sns$.$load$\_$dataset$("$car$\_$crashes"$)$

Submit a jupyter notebook with Python code $(.$ipynb extension$)$ that does the following:

$1.$ A multiple regression analysis. Your target variable is "total". Your predictor variables are all other variables other than the state abbreviation.

$2.$ You have decided to only include variables if you are at least $95\%$ confident that they are significant, i$.$e$.$ not just based on sampling error. If you eliminate any, show that code that eliminates them, and then add code to re$-$run your regression.

$3.$ Develop three visualizations that "tell the story" of your data.

Submit a Power Point Presentation $(.$pptx extension$)$ that includes the following:

$1.$ An explanation of what your regression formula means. Write it in the form:

y $=$ m$1$x$1+$ m$2$x$2+$ m$3$x$3...+$ b

Instead of the variables, show the names and actual numbers of what they represent.

$2.$ If you excluded any variables based on the issue of the $95\%$ confidence level, explain why you excluded them.

$3.$ Include the output of your three visualizations. Explain what each one means and why you believed it was important.

You must include both your jupyter notebook and your Power Point deck to get any credit for this project.