f.Which measure of center (mean or median) is more appropriate for these data?Why? Consider the shape of the distribution discussed in part c.8h.Calculate the standard deviation of the annual income data.Calculate the interquartile range of the annual income data.Visualizing Two VariablesLet's continue to explore the annual income data, but now consider how annual income datamay vary between loan status (current or fully paid).Construct a side-by-side boxplot for annual income broken up by loan status. Includeinformative labels and a title.J.How do the distributions of annual income compare for loan status? Comment onthe shape, center, spread, and presence of outliers for the two groups.k.Exploring a Single Categorical VariableFinally, we'll focus our attention only on the loan status variable.Construct a table of counts for the loan status variable. Report the number ofobservations in each category below.I.Construct a table of proportions for the loan status variable. Report the proportionsfor each category below.Construct a barplot that displays the distribution of loan status types. Includeinformative labels and a title. Include your barplot below. Part 2 of the Introduction to RStudio & Tutorial uses two variables from the Lending Club data:loan amount and homeownership. For the assignment you'll submit, you will practice usingtwo different variables. Please make sure the assignment you submit uses the correct variables(specified in the questions below).Exploring a Single Quantitative VariableFor this portion of the assignment, you'll practice using R to explore the annual_incomevariable in the loan50.csv data set.a.Construct a histogram of the annual income data. Include informative labels and atitle. Include your histogram below.b.Construct a boxplot of the annual income data. Include informative labels and atitle. Include your boxplot below.C.Using the histogram you constructed in part a and the boxplot from part b, describethe shape of the distribution of the annual income variable and comment on the presence ofany outliers.d.e.Calculate the mean of the annual income data.Calculate the median of the annual income data.

“Since you have asked multiple question, we will solve the first question for you. If you want any…

f. Which measure of center (mean or median) is more appropriate for these data? Why? Consider the shape of the distribution discussed in part c. h. Calculate the standard deviation of the annual income data. Calculate the interquartile range of the annual income data.

f. Which measure of center (mean or median) is more appropriate for these data? Why? Consider the shape of the distribution discussed in part c. h. Calculate the standard deviation of the annual income data. Calculate the interquartile range of the annual income data.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Payback Period

Payback period analysis is a method used to determine the relative time value of a project. It measures the time that a project will take to return its initial investment. The total time taken by the project to return the initial investment made for the project is called the payback period.

Question

f.
Which measure of center (mean or median) is more appropriate for these data?
Why? Consider the shape of the distribution discussed in part c.
8
h.
Calculate the standard deviation of the annual income data.
Calculate the interquartile range of the annual income data.
Visualizing Two Variables
Let's continue to explore the annual income data, but now consider how annual income data
may vary between loan status (current or fully paid).
Construct a side-by-side boxplot for annual income broken up by loan status. Include
informative labels and a title.
J.
How do the distributions of annual income compare for loan status? Comment on
the shape, center, spread, and presence of outliers for the two groups.
k.
Exploring a Single Categorical Variable
Finally, we'll focus our attention only on the loan status variable.
Construct a table of counts for the loan status variable. Report the number of
observations in each category below.
I.
Construct a table of proportions for the loan status variable. Report the proportions
for each category below.
Construct a barplot that displays the distribution of loan status types. Include
informative labels and a title. Include your barplot below.

Part 2 of the Introduction to RStudio & Tutorial uses two variables from the Lending Club data:
loan amount and homeownership. For the assignment you'll submit, you will practice using
two different variables. Please make sure the assignment you submit uses the correct variables
(specified in the questions below).
Exploring a Single Quantitative Variable
For this portion of the assignment, you'll practice using R to explore the annual_income
variable in the loan50.csv data set.
a.
Construct a histogram of the annual income data. Include informative labels and a
title. Include your histogram below.
b.
Construct a boxplot of the annual income data. Include informative labels and a
title. Include your boxplot below.
C.
Using the histogram you constructed in part a and the boxplot from part b, describe
the shape of the distribution of the annual income variable and comment on the presence of
any outliers.
d.
e.
Calculate the mean of the annual income data.
Calculate the median of the annual income data.

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