adplus-dvertising

Welcome to the Visualizing Data and Linear Regression MCQs Page

Dive deep into the fascinating world of Visualizing Data and Linear Regression with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Visualizing Data and Linear Regression, a crucial aspect of R Programming. In this section, you will encounter a diverse range of MCQs that cover various aspects of Visualizing Data and Linear Regression, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within R Programming.

frame-decoration

Check out the MCQs below to embark on an enriching journey through Visualizing Data and Linear Regression. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of R Programming.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Visualizing Data and Linear Regression. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Visualizing Data and Linear Regression MCQs | Page 2 of 7

Explore more Topics under R Programming

Q11.
Which of the following adds marginal sums to an existing table?

a.

par()

b.

prop.table()

c.

addmargins()

d.

quantile()

Discuss
Answer: (b).prop.table()
Q12.
Which of the following lists names of variables in a data.frame?

a.

quantile()

b.

names()

c.

barchart()

d.

par()

Discuss
Answer: (a).quantile()
Q13.
Which of the following is tool for chi-square distributions?

a.

pchisq()

b.

chisq()

c.

pnorm

d.

barchart()

Discuss
Answer: (c).pnorm
Q14.
Which of the following groups values of a variable into larger bins?

a.

cut

b.

col.max(x)

c.

stem

d.

which.max(x)

Discuss
Answer: (a).cut
Q15.
Which of the following determine the least-squares regression line?

a.

histo()

b.

lm

c.

barlm()

d.

col.max(x)

Discuss
Answer: (b).lm
Q16.
Which of the following is tool for checking normality?

a.

qqline()

b.

qline()

c.

anova()

d.

lm()

Discuss
Answer: (a).qqline()
Q17.
Which of the following is lattice command for producing boxplots?

a.

plot()

b.

bwplot()

c.

xyplot()

d.

barlm()

Discuss
Answer: (b).bwplot()
Q18.
Which of the following compute analysis of variance table for fitted model?

a.

ecdf()

b.

cum()

c.

anova()

d.

bwplot()

Discuss
Answer: (c).anova()
Q19.
Which of the following is used to find variance of all values?

a.

var()

b.

sd()

c.

mean()

d.

anova()

Discuss
Answer: (a).var()
Q20.
The purpose of fisher.test() is _______ test for contingency table.

a.

Chisq

b.

Fisher

c.

Prop

d.

Stem

Discuss
Answer: (b).Fisher
Page 2 of 7

Suggested Topics

Are you eager to expand your knowledge beyond R Programming? We've curated a selection of related categories that you might find intriguing.

Click on the categories below to discover a wealth of MCQs and enrich your understanding of Computer Science. Happy exploring!

Data Science

Discover the fascinating world of extracting insights from data with our Data Science...

HTML

Start your journey in web development with our HTML MCQs. Learn the building blocks...

C# programming

Hone your skills in the C# language with our MCQs. Understand data types, operators,...

Compiler Design

Unravel the process of translating source code into executable programs with our...

Computer Architecture

Unveil the science behind computer design with our Computer Architecture MCQs. Topics...

Python

Dive into one of the most popular languages with our Python MCQs. Understand...

Cryptography and Network Security

Secure your knowledge of information security with our Cryptography and Network...

Computer Graphics

Step into the visual side of computing with our Computer Graphics MCQs. Cover topics...

Operating System

Dive deep into the core of computers with our Operating System MCQs. Learn about...