# R Programming Questions and Answers – Distribution – 1

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This set of R Programming Language Multiple Choice Questions & Answers (MCQs) focuses on “Distribution – 1”.

1. The _______ and ________ of a discrete random variable is easy to compute at the console.
a) Mean, Variance
b) Variance, Packages
c) Packages, Functions
d) Median, Mode

Explanation: The mean and variance of a discrete random variable are easy to compute at the console. To calculate the mean µ, we need to multiply the corresponding values of x and f and add them.

2. Which of the following simple the square root of σ square?
a) Variance
b) Standard deviation
c) Mean
d) Median

Explanation: Standard deviation is the measure of dispersion of a set of data from its mean. The standard deviation σ is simply the square root of σ square.

3. The abbreviation of Cumulative Distributive Function is ________
a) CDFN
b) CMDF
c) CMD
d) CDF

Explanation: A cumulative distributive function P(x) is the probability of getting some value less than a certain value x. If p(x) will be the density function for a probability distribution, then that will be the cumulative distributive function P(x) is calculated as the integral of p(x) over the interval −∞ to x.

4. Which function is used to simulate discrete uniform random variables?
a) Sample
b) Simple
c) Function
d) Variance

Explanation: One can choose an integer at random with the sample function. The general syntax of the simulation of the discrete uniform random variable is a sample (x, size, replace = TRUE).

5. The binomial distribution is based on _______ trial.
a) Einstein
b) Bernoulli
c) Bohr
d) Kepler

Explanation: The binomial distribution is the distribution based on the Bernoulli trial, which is a random experiment in which there are only two possible outcomes: success (S) and failure (F).

6. The corresponding R function for the PMF is __________
a) Trinom
b) Dbinorm
c) Dbinom
d) Fnorm

Explanation: dnorm is the height of the density of a normal curve while dbinom returns the probability of an outcome of a binomial distribution.

7. The corresponding R function for the CDF is __________
a) Dbinom
b) Pbinom
c) Cbinorm
d) Hbinorm

Explanation: The function pbinom is very useful for summing consecutive binomial probabilities. The pbinom function returns the probability that the value of a variable that follows the binomial distribution.

8. Random variables defined via the _______ package.
a) Distri
b) Diestr
c) Distr
d) Diftr

Explanation: Random variables defined via the distr package may be plotted, which will return graphs of the PMF, CDF, and quantile function.

9. MGF means _____________
a) Moment Generating Formulas
b) Movement Generating Formulas
c) Moment Generating Functions
d) Moment Graphic Functions

Explanation: The moment generating function, if it exists in a neighbourhood of zero, determines a probability distribution uniquely.

10. The empirical cumulative distribution function can be called as __________
a) EMCDF
b) EDFC
c) ECDF
d) EGDF

Explanation: The empirical cumulative distribution function is the probability distribution that places into the probability mass. ECDF refers to the empirical cumulative distribution function.

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