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  Download and go through Week2Part2 video and follow the process I have to demo your dataset. Start from Week2_Part2.R script, change it according to your selected datset from Quiz2. Then submit to this link: 1. Your modified script. 2. A screenshot report showing the outputs from your rstudio after executing all activities. Week2_Part2.R script:    setwd(“C:/Users/ialsmadi/Desktop/University_of_Cumberlands/Lectures/Week2/RScripts”) getwd() # Import test data data<-read.csv("yearly_sales.csv") #A 5-number summary is a set of 5 descriptive statistics for summarizing a continuous univariate data set.  #It consists of the data set's: minimum, 1st quartile, median, 3rd quartile, maximum #Find the set, L, of data below the median. The 1st quartile is the median of L. #Find the set, U, of data above the median. The 3rd quartile is the median of U. print(summary(data)) anscombe<-read.csv("anscombe.csv") print(summary(anscombe)) sd(anscombe$X) var(anscombe$X) sd(anscombe$x1) var(anscombe$x1) sd(anscombe$x2) var(anscombe$x2) sd(anscombe$x3) var(anscombe$x3) sd(anscombe$x4) var(anscombe$x4) sd(anscombe$y1) var(anscombe$y1) sd(anscombe$y2) var(anscombe$y2) sd(anscombe$y3) var(anscombe$y3) ##-- now some "magic" to do the 4 regressions in a loop: ff <- y ~ x mods <- setNames(as.list(1:4), paste0("lm", 1:4)) for(i in 1:4) { ff[2:3] <- lapply(paste0(c("y","x"), i), ## or ff[[2]] <-"y", i)) ## ff[[3]] <-"x", i)) mods[[i]] <- lmi <- lm(ff, data = anscombe) print(anova(lmi)) } ## See how close they are (numerically!) sapply(mods, coef) lapply(mods, function(fm) coef(summary(fm))) ## Now, do what you should have done in the first place: PLOTS op <- par(mfrow = c(2, 2), mar = 0.1+c(4,4,1,1), oma = c(0, 0, 2, 0)) for(i in 1:4) { ff[2:3] <- lapply(paste0(c("y","x"), i), plot(ff, data = anscombe, col = "red", pch = 21, bg = "orange", cex = 1.2, xlim = c(3, 19), ylim = c(3, 13)) abline(mods[[i]], col = "blue") } mtext("Anscombe's 4 Regression data sets", outer = TRUE, cex = 1.5) par(op) plot(sort(data$num_of_orders)) hist(sort(data$num_of_orders)) plot(density(sort(data$num_of_orders))) plot(sort(data$gender)) hist(sort(data$sales_total)) plot(density(sort(data$sales_total))) library(lattice) densityplot(data$num_of_orders) # top plot # bottom plot as log10 is actually # easier to read, but this plot is in natural log densityplot(log(data$num_of_orders)) densityplot(data$sales_total) densityplot(log(data$sales_total)) hist(data$sales_total, breaks=100, main="Sales total",  xlab="sales", col="gray") # draw a line for the media abline(v = median(data$sales_total), col = "magenta", lwd = 4) # use rug() function to see the actual datapoints rug(data$sales_total) #Boxplots can be created for individual variables or for variables by group.  #The format is boxplot(x, data=), where x is a formula and data= denotes the data frame providing  #the data. boxplot(data$sales_total,data=data, main="Dis by Sales",  xlab="Sales", ylab="Total") # Boxplot of MPG by Car Cylinders, using one of R built-in datasets  boxplot(mpg~cyl,data=mtcars, main="Car Milage Data",  xlab="Number of Cylinders", ylab="Miles Per Gallon") #in our boxplot above, we might want to draw a horizontal line at 12 where the national standard is. abline(h = 12) boxplot(data$sales_total,data=data, main="Total sales Bplot",  xlab="Sales", ylab="Total") # Dot chart of a single numeric vector dotchart(mtcars$mpg, labels = row.names(mtcars), cex = 0.6, xlab = "mpg") #install.packages("ROCR") #library(ROCR) # Simple Scatterplot attach(mtcars) plot(wt, mpg, main="Scatterplot Example",  xlab="Car Weight ", ylab="Miles Per Gallon ", pch=19) #The R function abline() can be used to add vertical, horizontal or regression lines to a graph plot(data$sales_total, data$gender) # Add fit lines abline(lm(data$sales_total~ data$num_of_orders), col="red") # regression line (y~x)  lines(lowess(data$sales_total, data$num_of_orders), col="blue") # lowess line (x,y) # Basic Scatterplot Matrix pairs(data) pairs(data[0:2]) # Scatterplot Matrices from the car Package install.packages("car") library(car) install.packages("ggplot2") library(ggplot2) quit()

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