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Problem 1: Installed the ISLR library using the install.packages() command. Call the library to ensure that it was installed properly. Called ggplot2 library for Problem 5.

library(ISLR)
library(ggplot2)

Problem 2: Created a new R-Notebook for Assignment—Setting up R & then printed summary of Carseats & determined observation in the dataset

summary(Carseats)

##      Sales          CompPrice       Income        Advertising    
##  Min.   : 0.000   Min.   : 77   Min.   : 21.00   Min.   : 0.000  
##  1st Qu.: 5.390   1st Qu.:115   1st Qu.: 42.75   1st Qu.: 0.000  
##  Median : 7.490   Median :125   Median : 69.00   Median : 5.000  
##  Mean   : 7.496   Mean   :125   Mean   : 68.66   Mean   : 6.635  
##  3rd Qu.: 9.320   3rd Qu.:135   3rd Qu.: 91.00   3rd Qu.:12.000  
##  Max.   :16.270   Max.   :175   Max.   :120.00   Max.   :29.000  
##    Population        Price        ShelveLoc        Age          Education   
##  Min.   : 10.0   Min.   : 24.0   Bad   : 96   Min.   :25.00   Min.   :10.0  
##  1st Qu.:139.0   1st Qu.:100.0   Good  : 85   1st Qu.:39.75   1st Qu.:12.0  
##  Median :272.0   Median :117.0   Medium:219   Median :54.50   Median :14.0  
##  Mean   :264.8   Mean   :115.8                Mean   :53.32   Mean   :13.9  
##  3rd Qu.:398.5   3rd Qu.:131.0                3rd Qu.:66.00   3rd Qu.:16.0  
##  Max.   :509.0   Max.   :191.0                Max.   :80.00   Max.   :18.0  
##  Urban       US     
##  No :118   No :142  
##  Yes:282   Yes:258  
##                     
##                     
##                     
##

Problem 2: Used nrow() function to calculate rows in data set. There are 400 rows.

nrow(Carseats)

## [1] 400

print(paste("There are", nrow(Carseats),"rows."))

## [1] "There are 400 rows."

Problem 3: Can easily observe the Max.value of the Price element is 29 in above table; or can use the max() function to calculate.

max(Carseats$Advertising)

## [1] 29

print(paste("The max. value of the price element is", max(Carseats$Advertising)))

## [1] "The max. value of the price element is 29"

Problem 4 Used the IQR function to Calculate the IQR of the Price Attribute.31

IQR(Carseats$Price)

## [1] 31

print(paste( "The IQR of the price attribute is",IQR(Carseats$Price)))

## [1] "The IQR of the price attribute is 31"

Problem 5: Plotted Sales against Price using gglot(). I see a correlation: as the price increases, sales decrease.

ggplot(Carseats, aes(x = Price, y = Sales)) +
  geom_point(color = "steelblue") +
  labs(title = "Sales vs. Price",
       x = "Price",
       y = "Sales") +
theme_minimal()

print(paste("I see the following correlation: As price increases, sales decrease." ))

## [1] "I see the following correlation: As price increases, sales decrease."

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