# 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.

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

Q1.
________ generate random Normal variates with a given mean and standard deviation.
Q2.
Point out the correct statement?
Answer: (a).R comes with a set of pseudo-random number generators
Q3.
______ evaluate the cumulative distribution function for a Normal distribution.
Q4.
_______ generate random Poisson variates with a given rate.
Q5.
Point out the wrong statement?
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?
Q7.
_________ is the most common probability distribution to work with.
Q8.
What will be the output of the following R code?
> x <- rnorm(10)
> x
Answer: (a). 0.01874617 -0.18425254 -1.37133055 -0.59916772 0.29454513
 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)
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)