Question

In: Statistics and Probability

Assignment: Install and load the ggplot2 package. load the "diamonds" dataset RCode: install.packages("ggplot2") library(ggplot2) ?diamonds 1....

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)

Solutions

Expert Solution

library("ggplot2")
attach(diamonds)
View(diamonds)

ggplot(data=diamonds) + geom_histogram(binwidth=500,
aes(x=diamonds$price)) + ggtitle("Diamond Price Distribution") +
xlab("Diamond Price U$") + ylab("Frequency") + theme_minimal()

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

Model is

r-squared = 0.919 or 92% variation in y is explained by this model.

orchestra answered 2 years ago
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