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Welcome to the Distributions and Graphics in R programming MCQs Page

Dive deep into the fascinating world of Distributions and Graphics in R programming with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Distributions and Graphics in R programming, a crucial aspect of R Programming. In this section, you will encounter a diverse range of MCQs that cover various aspects of Distributions and Graphics in R programming, 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 Distributions and Graphics in R programming. 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 Distributions and Graphics in R programming. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Distributions and Graphics in R programming MCQs | Page 2 of 6

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Q11.
Two important methods in analysis is differentiation and __________ transformation.

a.

Bernoulli

b.

Fourier

c.

Bohr

d.

Rutherford

Discuss
Answer: (b).Fourier
Q12.
Every ________ function has a probability distribution function.

a.

Continuous

b.

Discrete

c.

Categorical

d.

Random

Discuss
Answer: (a).Continuous
Q13.
Which package resorts to numerical methods when it encounters a model it does not recognize.

a.

destrEx

b.

ditrEx

c.

distrEx

d.

Exdistr

Discuss
Answer: (c).distrEx
Q14.
When µ = ___ and σ = ___ we say that the random variable has a standard normal distribution.

a.

0,1

b.

0,0

c.

1,0

d.

1,1

Discuss
Answer: (a).0,1
Q15.
Which of the following is the symbol of standard normal PDF?

a.

φ

b.

#

c.

&

d.

*

Discuss
Answer: (a).φ
Q16.
The __________ package has functionality to investigate transformations of univariate distributions.

a.

Distri

b.

Dirtr

c.

Distr

d.

Hyperbolic

Discuss
Answer: (c).Distr
Q17.
Which distribution looks like a norm distribution but with very heavy tails?

a.

Simple

b.

Discrete

c.

Continuous

d.

Cauchy

Discuss
Answer: (d).Cauchy
Q18.
The ________ is represented by the location parameter.

a.

Median

b.

Mode

c.

Mean

d.

Variance

Discuss
Answer: (a).Median
Q19.
The ______ parameter influences the spread of the distribution about its median.

a.

Scale

b.

Mode

c.

Mean

d.

Variance

Discuss
Answer: (a).Scale
Q20.
Which distribution comes up a lot in Bayesian statistics because it is a good model for one’s prior beliefs about a population proportion?

a.

Bohr

b.

Discrete

c.

Alpha

d.

Beta

Discuss
Answer: (d).Beta
Page 2 of 6

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