In your initial post, address the following items: Cars data frame $($showing only the first five observations$)$

$\backslash$table$[[,$Unnamed: $0,$mpg$,$cyl$,$disp,hp$,$drat,wt$,$qsec,vs$,$am$,$gear,carb$],[14,$Cadillac Fleetwood,$10\mathrm{.}4,8,472\mathrm{.}0,205,2\mathrm{.}93,5\mathrm{.}250,17\mathrm{.}98,0,0,3,4],[23,$Camaro Z$28,13\mathrm{.}3,8,350\mathrm{.}0,245,3\mathrm{.}73,3\mathrm{.}840,15\mathrm{.}41,0,0,3,4],[10,$Merc $280$C$,17\mathrm{.}8,6,167\mathrm{.}6,123,3\mathrm{.}92,3\mathrm{.}440,18\mathrm{.}90,1,0,4,4],[16,$Chrysler Imperial,$14\mathrm{.}7,8,440\mathrm{.}0,230,3\mathrm{.}23,5\mathrm{.}345,17\mathrm{.}42,0,0,3,4],[2,$Datsun $710,22\mathrm{.}8,4,108\mathrm{.}0,93,3\mathrm{.}85,2\mathrm{.}320,18\mathrm{.}61,1,1,4,1]]640x480$ with $1$ Axes$>,mpg,wt$

$mpg,1\mathrm{.}00000,-0\mathrm{.}87938$

$wt-0\mathrm{.}87938,1\mathrm{.}00000$ OLS Regression Results

Warnings:

$[1]$ Standard Errors assume that the covariance matrix of the errors is correctly specified.In your initial post, address the following items:

You created a scatterplot of miles per gallon against weight;

check to make sure it was included in your attachment. Does

the graph show any trend? If yes, is the trend what you

expected? Why or why not? See Step $2$ in the Python script.

What is the coefficient of correlation between miles per gallon

and weight? What is the sign of the correlation coefficient?

Does the coefficient of correlation indicate a strong

correlation, weak correlation, or no correlation between the

two variables? How do you know? See Step $3$ in the Python

script.

Write the simple linear regression equation for miles per gallon

as the response variable and weight as the predictor variable.

How might the car rental company use this model? See Step $4$

in the Python script.

What is the slope coefficient? Is this coefficient significant at a

$5\%$ level of significance $($alpha$=0\mathrm{.}05)?($Hint: Check the P$-$value,

$P>|t|,$ for weight in the Python output.$)$ See Step $4$ in the

Python script.

You created a scatterplot of miles per gallon against weight;

check to make sure it was included in your attachment. Does

the graph show any trend? If yes, is the trend what you

expected? Why or why not? See Step $2$ in the Python script.

What is the coefficient of correlation between miles per gallon

and weight? What is the sign of the correlation coefficient?

Does the coefficient of correlation indicate a strong

correlation, weak correlation, or no correlation between the

two variables? How do you know? See Step $3$ in the Python

script.

Write the simple linear regression equation for miles per gallon

as the response variable and weight as the predictor variable.

How might the car rental company use this model? See Step $4$

in the Python script.

What is the slope coefficient? Is this coefficient significant at a

$5\%$ level of significance $($alpha$=0\mathrm{.}05)?($Hint: Check the P$-$value,

$P>|t|,$ for weight in the Python output.$)$ See Step $4$ in the

Python script.