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Open RStu • Open a new R Script. • Copy and paste the below code in R. (to present your answer for this task, take a screenshot of your RStudio session, and paste it into your assessment solutions document). • Run the code, then copy and paste the outputs and the graphs into your assessment solutions document. • Copy and paste the code in your assignment and use comments to explain each line of the code.

Open RStu • Open a new R Script. • Copy and paste the below code in R. (to present your answer for this task, take a screenshot of your RStudio session, and paste it into your assessment solutions document). • Run the code, then copy and paste the outputs and the graphs into your assessment solutions document. • Copy and paste the code in your assignment and use comments to explain each line of the code.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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• Open RStudio. • Open a new R Script. • Copy and paste the below code in R. (to present your answer for this task, take a screenshot of your RStudio session, and paste it into your assessment solutions document). • Run the code, then copy and paste the outputs and the graphs into your assessment solutions document. • Copy and paste the code in your assignment and use comments to explain each line of the code. Note: to get help in R, highlight the code and press F1. Code to be copied and pasted into an R script in RStudio: Treatment <- rep (c("Level_1", "Level_2","Level_3"), each=10) Response <- c(11, 8, 10, 12, 8, 12, 9, 9, 4, 12, 12, 9, 12, 10, 18, 24, 17, 16, 20, 11, 14, 7, 18, 8, 12, 10, 9, 14, 12, 10) Data Question_3<-data.frame(Treatment, Response) View(Data_Question 3) aggregate (Data Question_3$Response, list (Data Question_3$Treatment), mean) aggregate (Data_Question_3$Response, list (Data Question_3$Treatment), sd) aggregate (Response Treatment, Data Question 3, function (Response) mean (Response) table(Treatment) c(length=length (Response), mean-mean (Response), sd-sd (Response), var=var (Response))) boxplot (Response, col 15) LOG_Response <- log(Response) boxplot (Response-Treatment, col = 10) boxplot (LOG_Response, col = 12) hist (Response, prob= TRUE, main="Response Histogram", col="white", ylim = c(0,0.1)) x2 < seq(min(Response), max (Response), length = 40) Normal Curve <- dnorm(x2, mean = mean (Response), sd = sd (Response)) lines (x2, Normal_Curve, col = "green", lwd = 2) Q3 ANOVA <- aov (Response-Treatment, data-Data_Question 3) summary (Q3_ANOVA)
Transcribed Image Text:• Open RStudio. • Open a new R Script. • Copy and paste the below code in R. (to present your answer for this task, take a screenshot of your RStudio session, and paste it into your assessment solutions document). • Run the code, then copy and paste the outputs and the graphs into your assessment solutions document. • Copy and paste the code in your assignment and use comments to explain each line of the code. Note: to get help in R, highlight the code and press F1. Code to be copied and pasted into an R script in RStudio: Treatment <- rep (c("Level_1", "Level_2","Level_3"), each=10) Response <- c(11, 8, 10, 12, 8, 12, 9, 9, 4, 12, 12, 9, 12, 10, 18, 24, 17, 16, 20, 11, 14, 7, 18, 8, 12, 10, 9, 14, 12, 10) Data Question_3<-data.frame(Treatment, Response) View(Data_Question 3) aggregate (Data Question_3$Response, list (Data Question_3$Treatment), mean) aggregate (Data_Question_3$Response, list (Data Question_3$Treatment), sd) aggregate (Response Treatment, Data Question 3, function (Response) mean (Response) table(Treatment) c(length=length (Response), mean-mean (Response), sd-sd (Response), var=var (Response))) boxplot (Response, col 15) LOG_Response <- log(Response) boxplot (Response-Treatment, col = 10) boxplot (LOG_Response, col = 12) hist (Response, prob= TRUE, main="Response Histogram", col="white", ylim = c(0,0.1)) x2 < seq(min(Response), max (Response), length = 40) Normal Curve <- dnorm(x2, mean = mean (Response), sd = sd (Response)) lines (x2, Normal_Curve, col = "green", lwd = 2) Q3 ANOVA <- aov (Response-Treatment, data-Data_Question 3) summary (Q3_ANOVA)
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