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

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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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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