For this assignment, name your R file fastfoodStats.RFor 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 stateE.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]

The task requires you to run a logistic regression model in R, predicting whether or not wine was…

Answered: not installed, you’ll need to do a…

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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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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