Need to code for this practice assignment all the code needs to be assigned as per the directions. its going in code grade so Q$1$ needs to be assigned to Q$1$ the Q$2$ assigned to Q$2$ and so on thank you. fastfoodStats For this assignment, name your R file fastfoodStats.R For all questions you should load tidyverse, lm$.$beta and openintro. You should not need to use any other libraries. If the openintro package is not installed, you$$ll need to do a one$-$time installation from the Console Window in RStudio like this: install.packages$("$openintro$")$ If the lm$.$beta package is not installed, you$$ll need to do a one$-$time installation from the Console Window in RStudio like this: install.packages$("$lm$.$beta"$)$ Load libraries with suppressPackageStartupMessages$($library$($tidyverse$))$ suppressPackageStartupMessages$($library$($lm$.$beta$))$ suppressPackageStartupMessages$($library$($openintro$))$ Round all float$/$dbl values to two decimal places. All statistics should be run with variables in the order I state E$.$g$.,$Run a regression predicting mileage from mpg$,$ make, and type$$ would be: lm$($mileage ~ mpg $+$ make $+$ type...$)$ Before attempting to answer these, or if you lose points upon an attempt, please review all CodeGrade information provided in the CodeGrade overview submodule $-$ if you do not you are likely to lose points. To access the fastfood data, run the following: fastfood $<-$ openintro::fastfood 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$1$ Create a regression predicting whether or not a restaurant is McDonalds or Subway based on calories, sodium, and protein. $($McDonalds should be $1,$ Subway $0)$ Save the coefficients to Q$2$ Run the same regression as in #$2$ but remove sodium as a predictor. Which model fits better? Save the AIC of the better model to Q$3.$ Run a regression predicting calories from saturated fat, fiber, and sugar. Based on standardized regression coefficients, identify the strongest predictor. Assign the unstandardized regression coefficient of the strongest predictor to Q$4.($You can access the coefficients by indexing the model object$)$ 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$5.$