Credit Card Balance Data A data frame with 400 observations on a number of variables. • Income: Income in $1,000's • Limit: Credit limit • Rating: Credit rating • Cards: Number of credit cards • Age: Age in years . Education: Education in years • Own: A factor with levels No and Yes indicating whether the individual owns a home • Student: A factor with levels No and Yes indicating whether the individual is a student • Married: A factor with levels No and Yes indicating whether the individual is married • Region: A factor with levels East, South, and West indicating the individual's geographical location Balance: Average credit card balance in $.
Credit Card Balance Data A data frame with 400 observations on a number of variables. • Income: Income in $1,000's • Limit: Credit limit • Rating: Credit rating • Cards: Number of credit cards • Age: Age in years . Education: Education in years • Own: A factor with levels No and Yes indicating whether the individual owns a home • Student: A factor with levels No and Yes indicating whether the individual is a student • Married: A factor with levels No and Yes indicating whether the individual is married • Region: A factor with levels East, South, and West indicating the individual's geographical location Balance: Average credit card balance in $.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Please put the following answers in R coding. Question is in the image
![Credit Card Balance Data
A data frame with 400 observations on a number of variables.
• Income: Income in $1,000's
• Limit: Credit limit
• Rating: Credit rating
• Cards: Number of credit cards
Age: Age in years
Education: Education in years
• Own: A factor with levels No and Yes indicating whether the individual owns a home
• Student: A factor with levels No and Yes indicating whether the individual is a student
• Married: A factor with levels No and Yes indicating whether the individual is married
• Region: A factor with levels East, South, and West indicating the individual's geographical location
• Balance: Average credit card balance in $.
library(tidyr)
library (ISLR2)
dat <- drop_na (Credit)
head(dat)
##
Income Limit Rating Cards Age Education Own Student Married Region Balance
14.891 3606
2 34
11 No
Yes South
3 82
15 Yes
Yes
West
4 71
11 No
No West
## 1
## 2 106.025 6645
## 3 104.593 7075
## 4 148.924 9504
## 5 55.882 4897
## 6 80.180 8047
3 36
11 Yes
No
West
16 No
Yes South
2 68
4 77
10 No
No South
283
483
514
681
357
569
No
Yes
No
No
No
No
x1 <- dat $Balance [dat $Student=="Yes"]
x2 < dat $Balance [dat $Student=="No"]
333
903
580
964
331
1151
Use the following R code separate Balance for Student and Non-student and create histogram of Balance
for Student and Non-student. We wish to compare mean Balance of Student and Non-student group.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fba18de34-fc06-47a6-b1ea-c54726b84874%2F894c8f81-4ece-4aeb-be1a-2d6c2b8b6947%2F85ju01q_processed.png&w=3840&q=75)
Transcribed Image Text:Credit Card Balance Data
A data frame with 400 observations on a number of variables.
• Income: Income in $1,000's
• Limit: Credit limit
• Rating: Credit rating
• Cards: Number of credit cards
Age: Age in years
Education: Education in years
• Own: A factor with levels No and Yes indicating whether the individual owns a home
• Student: A factor with levels No and Yes indicating whether the individual is a student
• Married: A factor with levels No and Yes indicating whether the individual is married
• Region: A factor with levels East, South, and West indicating the individual's geographical location
• Balance: Average credit card balance in $.
library(tidyr)
library (ISLR2)
dat <- drop_na (Credit)
head(dat)
##
Income Limit Rating Cards Age Education Own Student Married Region Balance
14.891 3606
2 34
11 No
Yes South
3 82
15 Yes
Yes
West
4 71
11 No
No West
## 1
## 2 106.025 6645
## 3 104.593 7075
## 4 148.924 9504
## 5 55.882 4897
## 6 80.180 8047
3 36
11 Yes
No
West
16 No
Yes South
2 68
4 77
10 No
No South
283
483
514
681
357
569
No
Yes
No
No
No
No
x1 <- dat $Balance [dat $Student=="Yes"]
x2 < dat $Balance [dat $Student=="No"]
333
903
580
964
331
1151
Use the following R code separate Balance for Student and Non-student and create histogram of Balance
for Student and Non-student. We wish to compare mean Balance of Student and Non-student group.
![Credit Card Balance Data
A data frame with 400 observations on a number of variables.
• Income: Income in $1,000's
• Limit: Credit limit
• Rating: Credit rating
• Cards: Number of credit cards
Age: Age in years
Education: Education in years
• Own: A factor with levels No and Yes indicating whether the individual owns a home
• Student: A factor with levels No and Yes indicating whether the individual is a student
• Married: A factor with levels No and Yes indicating whether the individual is married
• Region: A factor with levels East, South, and West indicating the individual's geographical location
• Balance: Average credit card balance in $.
library(tidyr)
library (ISLR2)
dat <- drop_na (Credit)
head(dat)
##
Income Limit Rating Cards Age Education Own Student Married Region Balance
14.891 3606
2 34
11 No
Yes South
3 82
15 Yes
Yes
West
4 71
11 No
No West
## 1
## 2 106.025 6645
## 3 104.593 7075
## 4 148.924 9504
## 5 55.882 4897
## 6 80.180 8047
3 36
11 Yes
No
West
16 No
Yes South
2 68
4 77
10 No
No South
283
483
514
681
357
569
No
Yes
No
No
No
No
x1 <- dat $Balance [dat $Student=="Yes"]
x2 < dat $Balance [dat $Student=="No"]
333
903
580
964
331
1151
Use the following R code separate Balance for Student and Non-student and create histogram of Balance
for Student and Non-student. We wish to compare mean Balance of Student and Non-student group.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fba18de34-fc06-47a6-b1ea-c54726b84874%2F894c8f81-4ece-4aeb-be1a-2d6c2b8b6947%2F7t9hyzym_processed.png&w=3840&q=75)
Transcribed Image Text:Credit Card Balance Data
A data frame with 400 observations on a number of variables.
• Income: Income in $1,000's
• Limit: Credit limit
• Rating: Credit rating
• Cards: Number of credit cards
Age: Age in years
Education: Education in years
• Own: A factor with levels No and Yes indicating whether the individual owns a home
• Student: A factor with levels No and Yes indicating whether the individual is a student
• Married: A factor with levels No and Yes indicating whether the individual is married
• Region: A factor with levels East, South, and West indicating the individual's geographical location
• Balance: Average credit card balance in $.
library(tidyr)
library (ISLR2)
dat <- drop_na (Credit)
head(dat)
##
Income Limit Rating Cards Age Education Own Student Married Region Balance
14.891 3606
2 34
11 No
Yes South
3 82
15 Yes
Yes
West
4 71
11 No
No West
## 1
## 2 106.025 6645
## 3 104.593 7075
## 4 148.924 9504
## 5 55.882 4897
## 6 80.180 8047
3 36
11 Yes
No
West
16 No
Yes South
2 68
4 77
10 No
No South
283
483
514
681
357
569
No
Yes
No
No
No
No
x1 <- dat $Balance [dat $Student=="Yes"]
x2 < dat $Balance [dat $Student=="No"]
333
903
580
964
331
1151
Use the following R code separate Balance for Student and Non-student and create histogram of Balance
for Student and Non-student. We wish to compare mean Balance of Student and Non-student group.
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