Database System Concepts
Database System Concepts
7th Edition
ISBN: 9780078022159
Author: Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher: McGraw-Hill Education
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astfoodStats Assignment Description

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.

suppressPackageStartupMessages(library(tidyverse))

suppressPackageStartupMessages(library(openintro))

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...)

To access the fastfood data, run the following:

fastfood <- openintro::fastfood
  1. 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 Q1. The output should look like this:
               calories    total_fat     sugar     calcium
calories      1.00        0.81          0.45       0.61
total_fat      0.81        1.00          0.10       0.24
sugar         0.45        0.10          1.00       0.67
calcium       0.61        0.24         0.67       1.00
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Introduction

Co-relational matrix:

A correlation matrix is a matrix that depicts the linear relation of a number of variables. Pearson's correlation, which quantifies the sequential association between two variables, is displayed by the submissions inside the mixture. Pearson's correlations can vary from -1 to 1, with -1 representing a perfect negative linear relationship, 1 representing a perfect positive linear relationship, and 0 representing no linear relationship. In exploratory data analysis, the correlation matrix is frequently used to identify and visualize relationships between variables.

 

 

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