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

Programming with Microsoft Visual Basic 2017
8th Edition
ISBN:9781337102124
Author:Diane Zak
Publisher:Diane Zak
Chapter10: Classes And Objects
Section: Chapter Questions
Problem 2E: Open the Palace Solution.sln file contained in the VB2017\Chap10\Palace Solution folder. Use Windows...
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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...)

 

4. 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 Q4. (You can access the coefficients by indexing the model object)

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