### Q$1$ Create a correlation matrix for the relations between calories, total$\_$fat, sugar, and calcium for all items at Sonic, Subway, and Taco Bell, omitting missing values with $`$na$.$omit$()`.$

$-$ Assign the matrix to Q$1.$ It should look something like this:

$```$

calories total$\_$fat sugar calcium

calories $[$value$][$value$][$value$][$value$]$

total$\_$fat $[$value$][$value$][$value$][$value$]$

sugar $[$value$][$value$][$value$][$value$]$

calcium $[$value$][$value$][$value$][$value$]$

### Q$2$ Create a regression predicting whether or not a restaurant is McDonalds or Subway based on calories, sodium, and protein. $($McDonalds should be $1,$ Subway $0)$ Hint: make sure you know how McDonalds is spelled in the dataset.

$-$ Assign the model coefficients to Q$2.$ Your output should look something like this:

$```$

$($Intercept$)$ calories sodium protein

$[$value$][$value$][$value$][$value$]$

### Q$3$ Run the same regression as in Q$2$ but remove sodium as a predictor. Which is the better model?

$-$ Use the classical AIC $($k$=2).$

Assign the AIC of the better model to Q$3.$

### Q$4$ Run a regression predicting calories from saturated fat, fiber, and sugar. Based on $*$standardized$*$ regression coefficients, identify the strongest predictor.

$-$ Assign the $***$un$**$standardized$*$ regression coefficient of the strongest predictor to Q$4.$

$-($You can access the coefficients by indexing the model object.$)$

### Q$5$ For this question, use data from only restaurants with between $50$ and $60$ items in the data set. Predict total fat from cholesterol, total carbs, vitamin a$,$ and restaurant. Remove any nonsignificant predictors and run again.

$-$ Assign the strongest standardized regression coefficient to Q$5.$ Your output should look something like this:

$```$

$[$variable name$]$

$[$value$]$