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Welcome to the Profiling,Simulation and Data Analysis MCQs Page

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

Profiling,Simulation and Data Analysis MCQs | Page 1 of 11

Explore more Topics under R Programming

Q1.
________ generate random Normal variates with a given mean and standard deviation.

a.

dnorm

b.

rnorm

c.

pnorm

d.

rpois

Discuss
Answer: (b).rnorm
Q2.
Point out the correct statement?

a.

R comes with a set of pseudo-random number generators

b.

Random number generators cannot be used to model random inputs

c.

Statistical procedure does not require random number generation

d.

For each probability distribution there are typically three functions

Discuss
Answer: (a).R comes with a set of pseudo-random number generators
Q3.
______ evaluate the cumulative distribution function for a Normal distribution.

a.

dnorm

b.

rnorm

c.

pnorm

d.

rpois

Discuss
Answer: (c).pnorm
Q4.
_______ generate random Poisson variates with a given rate.

a.

dnorm

b.

rnorm

c.

pnorm

d.

rpois

Discuss
Answer: (d).rpois
Q5.
Point out the wrong statement?

a.

For each probability distribution there are typically three functions

b.

For each probability distribution there are typically four functions

c.

r function is sufficient for simulating random numbers

d.

R comes with a set of pseudo-random number generators

Discuss
Answer: (a).For each probability distribution there are typically three functions
Q6.
Which of the following evaluate the Normal probability density (with a given mean/SD) at a point?

a.

dnorm

b.

rnorm

c.

pnorm

d.

rpois

Discuss
Answer: (a).dnorm
Q7.
_________ is the most common probability distribution to work with.

a.

Gaussian

b.

Parametric

c.

Paradox

d.

Simulation

Discuss
Answer: (a).Gaussian
Q8.
What will be the output of the following R code? 
> x <- rnorm(10)
> x

a.

[1] 0.01874617 -0.18425254 -1.37133055 -0.59916772 0.29454513
[6] 0.38979430 -1.20807618 -0.36367602 -1.62667268 -0.25647839

b.

[1] 22.20356 21.51156 19.52353 21.97489 21.48278 20.17869 18.09011
[8] 19.60970 21.85104 20.96596

c.

Min. 1st Qu. Median Mean 3rd Qu. Max.
18.09 19.75 21.22 20.74 21.77 22.20

d.

Error

Discuss
Answer: (a).[1] 0.01874617 -0.18425254 -1.37133055 -0.59916772 0.29454513
[6] 0.38979430 -1.20807618 -0.36367602 -1.62667268 -0.25647839
Q9.
What will be the output of the following R code? 
> x <- rnorm(10)
> summary(x)

a.

[1] 0.01874617 -0.18425254 -1.37133055 -0.59916772 0.29454513
[6] 0.38979430 -1.20807618 -0.36367602 -1.62667268 -0.25647839

b.

[1] 22.20356 21.51156 19.52353 21.97489 21.48278 20.17869 18.09011
[8] 19.60970 21.85104 20.96596

c.

Min. 1st Qu. Median Mean 3rd Qu. Max.
18.09 19.75 21.22 20.74 21.77 22.20

d.

Error

Discuss
Answer: (c).Min. 1st Qu. Median Mean 3rd Qu. Max.
18.09 19.75 21.22 20.74 21.77 22.20
Q10.
What will be the output of the following R code? 
> pnorm(2)

a.

0.9772499

b.

1.9772499

c.

0.6772499

d.

2.6772499

Discuss
Answer: (a).0.9772499
Page 1 of 11

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