Question

Assignment:

Install and load the ggplot2 package.

load the "diamonds" dataset

RCode:

install.packages("ggplot2")

library(ggplot2)

?diamonds

1. Explore the dataset & state insights

2. Create plots for dataset

3: Provide summary of descriptive stats)

4. Run the regressions, research, Investigate & comment on R^2 & on regression plots - 1 line each.

#===========================================

# DV = Price, IV or IVs = your choice

# Can we create and compare models to predict "Price"?

# Question- Investigate & comment on R^2 & on plots

#Compare regression models & discuss R^2 -any improvement?

# Based on your understanding of regression models, select the best model

#to predict the price of diamonds based on the dataset

#Name your R file as LastNameFirstInitial.R and include your full name in the first line of the script.

diamonds {ggplot2} R Documentation

Prices of 50,000 round cut diamonds

Description

A dataset containing the prices and other attributes of almost 54,000 diamonds. The variables are as follows:

Usage

diamonds

Format

A data frame with 53940 rows and 10 variables:

price

price in US dollars (\$326–\$18,823)

carat

weight of the diamond (0.2–5.01)

cut

quality of the cut (Fair, Good, Very Good, Premium, Ideal)

color

diamond colour, from J (worst) to D (best)

clarity

a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))

x

length in mm (0–10.74)

y

width in mm (0–58.9)

z

depth in mm (0–31.8)

depth

total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43–79)

table

width of top of diamond relative to widest point (43–95)

Answer 1

code i wrote in R

#ANAND KUMAR

library(ggplot2)
library(onehot)
?diamonds
data = diamonds
data = as.data.frame(data)
View(data)

corrplot(data, na.rm = T)

#model 1 of price vs carat
summary(lm(data$price ~ data$carat))$r.squared

ggplot(data, aes(x = data$carat, y = data$price)) + geom_point() +
ggtitle("Scatter Diagram with estimated regression line on carat vs price") + xlab("carat") + ylab("price") +
stat_smooth(method = "lm", se = F)

#model 2 of x vs carat
summary(lm(data$price ~ data$x))$r.squared

ggplot(data, aes(x = data$x, y = data$price)) + geom_point() +
ggtitle("Scatter Diagram with estimated regression line on x vs price") + xlab("x") + ylab("price") +
stat_smooth(method = "lm", se = F)

#model 3 of y vs carat
summary(lm(data$price ~ data$y))$r.squared

ggplot(data, aes(x = data$y, y = data$price)) + geom_point() +
ggtitle("Scatter Diagram with estimated regression line on y vs price") + xlab("y") + ylab("price") +
stat_smooth(method = "lm", se = F)

#model 4 of z vs carat
summary(lm(data$price ~ data$z))$r.squared

ggplot(data, aes(x = data$z, y = data$price)) + geom_point() +
ggtitle("Scatter Diagram with estimated regression line on z vs price") + xlab("z") + ylab("price") +
stat_smooth(method = "lm", se = F)

#model 5 combining price with carat, x, y, z
summary(lm(data$price ~ data$z + data$y + data$x + data$carat))$r.squared

summary(data$color)
onehot(data$color)
inherits(data, "data.frame")

The model 1 had an R squared value of 0.84 and the combined model had an R squared value of 0.85 which fits data reasonably well, and depth and table values are significantly correlated with x,y,z values which isn't good while doing regression, so those columns haven't been taken into account.