not installed, you’ll need to do a one-time installation from the Console Window in RStudio like this: install.packages("tidyverse") You cannot attempt to install packages in code that you submit to CodeGrade.  If the openintro package is not installed

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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For this assignment, name your R file fastfoodStats.R

  • For all questions you should load tidyverse, openintro, and lm.beta.  You should not need to use any other libraries.
    • If the tidyverse package is not installed, you’ll need to do a one-time installation from the Console Window in RStudio like this:
      install.packages("tidyverse")
      You cannot attempt to install packages in code that you submit to CodeGrade. 
    • 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")
      You cannot attempt to install packages in code that you submit to CodeGrade. 
  • 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:
    packages("lm.beta")
    You cannot attempt to install packages in code that you submit to CodeGrade.
  • Load tidyverse with:

suppressPackageStartupMessages(library(tidyverse))

  • Load openintro with:

suppressPackageStartupMessages(library(openintro))

  • Load lm.beta with:

suppressPackageStartupMessages(library(lm.beta))

The actual data set is called fastfood.

  • Continue to use %>% for the pipe. CodeGrade does not support the new pipe. 
  • 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...)

1. Run a regression predicting whether or not wine was ordered from temperature, bill, and pizza.  

  • Assign the coefficients of the summary of the model to Q3. It should look something like this:  

        Estimate Std. Error z value Pr(>|z|)

(Intercept)    [value] [value] [value] [value]

temperature    [value] [value] [value] [value]

bill           [value] [value] [value] [value]

pizzas         [value] [value] [value] [value]

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