Question

Homework Assignment 2

Question 1:

The US Census Bureau occasionally surveys business owners and learns which firm women predominantly own. These data are a portion of the results reported in 2010 from 2007 survey of business owners. This table reports the number of women –owned businesses in 10 industries, along with the counts of the businesses in these industries:

Industry	Women- Owned	All
Agriculture	26,783	258,846
Arts and Entertainment	376,259	1,236,352
Construction	268,809	3,414,299
Finance	200,473	1,019,055
Manufacturing	113,495	615,936
Mining, oil and gas	18,313	122,470
Retail trade	918,657	2,672,892
Service	1,096,422	3,790,845
Transport	142,566	1,253,553
Wholesale trade	133,462	733,577

1. Make a Bar Chart to display the counts of business owned by women and a second bar chart that shows the counts for all businesses. In addition, label then correctly.
2. Make a Pareto chart to indicate which of the top three industries in which women business is highly dominant.
3. Construct a single chart that directly shows which industries have the highest concentration of women owned businesses.

Question 2:

How do the average credit scores of people living in various cities in the US differ? The file Credit Score Data of 143 American cities is provided in Canvas.

1. Construct a histogram
2. Create a Five Summary Report
3. Calculate Mean, Variance and Standard Deviation
4.   What conclusion can you reach concerning the average credit scores of people living in different American cities?

Answer 1

As HOMEWORKLIB's policy answering only Question 1:

(a)

R Code:

#Import Data
library(readxl)
company <- read_excel("Desktop/company.xlsx")
colnames(company)[2] <- "Women_Owned"

#Bar Chart for Women Owned
library(ggplot2)
ggplot(company, aes(x = Industry, y = Women_Owned)) +
geom_bar(stat = "identity")

#Bar Chart for All
library(ggplot2)
ggplot(company, aes(x = Industry, y = All)) +
geom_bar(stat = "identity")

(b)

#Pareto Chart
Women_Owned <- company$Women_Owned
names(Women_Owned) <- company$Industry

library(qcc)
pareto.chart(Women_Owned, ylab = "Women Owned Industry", xlab = "Industry")

so, service, Retail trade and Arts & Entertainment are the top three industries where women are highly dominant.

(c)

#Single Chart
library(dplyr)
women_concentration <- company %>%
mutate(Women_Concentration = Women_Owned * 100/ All) %>%
select(Industry, Women_Concentration) %>%
arrange(desc(Women_Concentration))

colnames(women_concentration)[2] <- "Women_Concentration (in %)"

** If any confusion please comment. I still wanted to answer question number 2 but there is no data. So as HOMEWORKLIB RULES's policy I answered only question 1. Thank you