Beauchamp Section #008 Week 7

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School

Southern Illinois University, Edwardsville *

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Course

107

Subject

Industrial Engineering

Date

Oct 30, 2023

Type

docx

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Uploaded by AdmiralExploration11576

Laboratory Exercise Week 7 Emma Beauchamp Section #008 10/05/2023 Directions : • Write your R code inside the code chunks after each question. • Write your answer comments after the # sign. • To generate the word document output, click the button Knit and wait for the word document to appear. • RStudio will prompt you (only once) to install the knitr package. • Submit your completed laboratory exercise using Blackboard’s Turnitin feature. Your Turnitin upload link is found on your Blackboard Course shell under the Laboratory folder. For this exercise, you will need to use the package mosaic to find numerical and graphical summaries. # install package if necessary if ( ! require (mosaic)) install.packages ( ` mosaic ` ) # load the package in R library (mosaic) # load the package mosaic to use its functions 1. Consider the population of CEO salaries in the lesson this week. i) Select a random sample of 40 CEO’s and compute the sample mean salary and sample standard deviation salary. ii) Use the do() function to compute the sample mean of 100 randomly selected samples. Plot the histogram of these 100 sample means. iii) Compute the average and standard deviation of these 100 sample means. Are the values what you expect from the properties of the sampling distribution. Code chunk # start your code ceo_salary <- read.csv ( "https://www.siue.edu/~jpailde/CEO_Salary_2012.csv" ) # i head (ceo_salary) ## Rank Name Company Annual.pay Age ## 1 1 John H Hammergren McKesson 131.190 53 ## 2 2 Ralph Lauren Ralph Lauren 66.650 72

## 3 3 Michael D Fascitelli Vornado Realty 64.405 55 ## 4 4 Richard D Kinder Kinder Morgan 60.940 67 ## 5 5 David M Cote Honeywell 55.790 59 ## 6 6 George Paz Express Scripts 51.525 57 samp1 <- sample_n (ceo_salary, size = 40 , replace = F) mean.salary <- mean ( ~ Annual.pay, data = samp1) sd.salary <- sd ( ~ Annual.pay, data = samp1) # ii simulated.means <- do ( 100 ) * mean ( ~ Annual.pay, data = sample_n (ceo_salary, size = 40 )) head (simulated.means) ## mean ## 1 8.79490 ## 2 12.36057 ## 3 10.38793 ## 4 10.21055 ## 5 9.62955 ## 6 11.14570 histogram ( ~ mean, data = simulated.means, main = "Sample Mean Salary Distribution" , xlab = "Sample Mean Salary ($Mil) of 40 CEO's" )

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