# R Programming Questions and Answers – Distribution – 2

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This set of R Programming written test Questions & Answers focuses on “Distribution – 2”.

1. Two important methods in analysis is differentiation and __________ transformation.
a) Bernoulli
b) Fourier
c) Bohr
d) Rutherford

Explanation: Two important methods in the analysis is differentiation and Fourier transformation. Unfortunately, not all functions are differentiable or have a Fourier transform.

2. Every ________ function has a probability distribution function.
a) Continuous
b) Discrete
c) Categorical
d) Random

Explanation: Every continuous random variable x has a great Probability Density Function (PDF). The PDF can sometimes be greater than 1. This is in contrast to the discrete case.

3. Which package resorts to numerical methods when it encounters a model it does not recognize.
a) destrEx
b) ditrEx
c) distrEx
d) Exdistr

Explanation: distrEx provides some extensions of package distr and further functionalities like var, sd, IQR, mad, median, skewness, kurtosis truncated moments.

4. 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

Explanation: When µ = 0 and σ = 1 we say that the random variable has a standard normal distribution and we typically write Z ∼ norm(mean = 0, sd = 1).

5. Which of the following is the symbol of standard normal PDF?
a) φ
b) #
c) &
d) *

Explanation: The lowercase Greek letter phi (φ) is used to denote the standard normal PDF and the capital Greek letter phi Φ is used to denote the standard normal CDF.

6. The __________ package has functionality to investigate transformations of univariate distributions.
a) Distri
b) Dirtr
c) Distr
d) Hyperbolic

Explanation: The distr package has a functionality to find the transformations of one variable distribution. There are exact results for the ordinary transformations for the standard distributions, and distr takes advantage of these in many cases.

7. Which distribution looks like a norm distribution but with very heavy tails?
a) Simple
b) Discrete
c) Continuous
d) Cauchy

Explanation: The Cauchy distribution looks like a norm distribution but with very heavy tails. The mean (and variance) do not exist, that is, they are infinite.

8. The ________ is represented by the location parameter.
a) Median
b) Mode
c) Mean
d) Variance

Explanation: The median is represented by the location parameter, and the scale parameter influences the spread of the distribution about its median.

9. The ______ parameter influences the spread of the distribution about its median.
a) Scale
b) Mode
c) Mean
d) Variance

Explanation: The scale parameter influences the spread of the distribution about its median. The median is represented by the location parameter.

10. 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

Explanation: Beta distribution comes up a lot in Bayesian statistics because it is a good model for one’s prior beliefs about a population proportion.

11. The associated R function is dlogis (x, location = 0, scale = 1) is for _________ distribution.
a) Logistic
b) Linear
c) Discrete
d) Beta

Explanation: The associated R function is dlogis (x, location = 0, scale = 1). The logistic distribution comes up in differential equations as a model for population growth under certain assumptions.

12. The _________ distribution comes up in differential equations as a model for population growth under certain assumptions.
a) Logistic
b) Linear
c) Discrete
d) Beta

Explanation: The associated R function is dlogis(x, location = 0, scale = 1). The logistic distribution comes up in differential equations as a model for population growth under certain assumptions.

13. Which of the following is a distribution derived from the normal distribution?
a) Logistic
b) Lognormal
c) Normal
d) Simple

Explanation: Lognormal distribution is a distribution derived from the normal distribution (hence the name). Its name also says that it is derived from the normal distribution.

14. The associated R function is dlnorm(x, meanlog = 0, sdlog = 1) is for ________ distribution.
a) Logistic
b) Lognormal
c) Normal
d) Simple

Explanation: The associated R function is dlnorm(x, meanlog = 0, sdlog = 1). Lognormal distribution is a distribution derived from the normal distribution (hence the name). Its name also says that it is derived from the normal distribution.

15. The associated R function is dweibull(x, shape, scale = 1) is for _________ distribution.
a) Logistic
b) Lognormal
c) Weibull
d) Simple

Explanation: The associated R function is dweibull(x, shape, scale = 1). There are exact results for ordinary transformations of the standard distributions, and distr takes advantage of these in many cases.

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