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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.

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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 3 of 7

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Q21.
Which of the following convert a matrix of phi coefficients to polychoric correlations?

a.

poly()

b.

qline()

c.

phi2poly

d.

multi.plot()

Discuss
Answer: (c).phi2poly
Q22.
Which of the following is used to plot multiple histograms?

a.

multi.plot()

b.

multi.hist

c.

xyplot.multi()

d.

poly()

Discuss
Answer: (b).multi.hist
Q23.
Which of the following count the number of good cases when doing pairwise analysis?

a.

count.pairwise

b.

count() +

c.

anova.para()

d.

count.poly()

Discuss
Answer: (a).count.pairwise
Q24.
Which of the following gives the summary of values likes mean etc?

a.

mean

b.

sd

c.

describe

d.

lm

Discuss
Answer: (c).describe
Q25.
The purpose of correct.cor is to correct _________ in values.

a.

difference

b.

reliability

c.

error

d.

similar

Discuss
Answer: (b).reliability
Q26.
What plot(s) are used to view the linear regression?

a.

Scatterplot

b.

Box plot

c.

Density plot

d.

Scatterplot, Boxplot, Density plot

Discuss
Answer: (d).Scatterplot, Boxplot, Density plot
Q27.
Common Metrics which are used to select linear model are ____________

a.

R-Squared Lower the better
F-Statistic Higher the better

b.

R-Squared Lower the better
F-Statistic Lower the better

c.

R-Squared Higher the better
F-Statistic Higher the better

d.

R-Squared Higher the better
F-Statistic Lower the better

Discuss
Answer: (c).R-Squared Higher the better
F-Statistic Higher the better
Q28.
In lm(response ~ terms), terms specification of the form “first*second” is same as __________

a.

first+second

b.

first:second

c.

first+second+first:second

d.

first:second+second:first

Discuss
Answer: (c).first+second+first:second
Q29.
In practice, Line of best fit or regression line is found when _____________

a.

Sum of residuals (∑(Y – h(X))) is minimum

b.

Sum of the absolute value of residuals (∑|Y-h(X)|) is maximum

c.

Sum of the square of residuals ( ∑ (Y-h(X))2) is minimum

d.

Sum of the square of residuals ( ∑ (Y-h(X))2) is maximum

Discuss
Answer: (c).Sum of the square of residuals ( ∑ (Y-h(X))2) is minimum
Q30.
If Linear regression model perfectly first i.e., train error is zero, then _____________________

a.

Test error is also always zero

b.

Test error is non zero

c.

Couldn’t comment on Test error

d.

Test error is equal to Train error

Discuss
Answer: (c).Couldn’t comment on Test error
Page 3 of 7

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