Advanced Statistics and Probability Interview Question-Answer

Q.1 Independent variables refer to those variables _________.

       A. Which acts as an input in the experiment

       B. Whose values are not related to the experiment

       C. Whose values are known during the experiment

Ans : Which acts as an input in the experiment


Q.2 What is multivariate statistics?

       A. Simultaneous observations on several variables

       B. Simultaneous analysis on several variables

       C. Finding relation between several variables

       D. All the options

Ans : All the options


Q.3 Use of only one variable to describe data is known as _____________.

       A. Multivariate data analysis

       B. Univariate data analysis

       C. Infinite data analysis

       D. Bivariate data analysis

Ans : Univariate data analysis


Q.4 Multivariate data analysis is an application of ________.

       A. Dimension reduction

       B. Multivariate Statistics

       C. Categorizing Variables

       D. All the options

Ans : Multivariate Statistics


Q.5 What are the features of a multivariate random variable?

       A. It is a set of unknown variables

       B. The variables have not occurred yet

       C. Both the options

Ans : Both the options


Q.6 Dependent variables refer to those variables __________.

       A. Whose variation is never analyzed

       B. Whose variation is analyzed

       C. Both the options

Ans : Whose variation is analyzed


Q.7 If the area under the PDF curve is zero, then __________.

       A. Probability = 1

       B. Probability = 0

       C. Probability = not defined

       D. Data insufficient

Ans : Probability = 0


Q.8 What is done when a new data in the sub Interval is added?

       A. Bin is inserted from the bottom

       B. One bin is added on the top

       C. Height of the previous bin is doubled

       D. Height of the bin is increased

Ans : One bin is added on the top


Q.9 A pattern such as a group or a trend in the data table cannot be studied using Multivariate data analysis.

       A. True

       B. False

Ans : False


Q.10 The least number of coordinates required to showcase a point is _________.

       A. Multivariate analysis

       B. Co-ordinates

       C. Dimension

       D. Bivariate analysis

Ans : Dimension


Q.11 Stochastic variables are also known as ___________.

       A. Random variables

       B. Variables

       C. Both the options

       D. None of the options

Ans : Random variables


Q.12 What is the drawback of using Kernel density estimation’s Histogram method?

       A. It is independent of the bin width

       B. Plot is discontinuous

       C. Plot is not smooth

       D. Plot is over smooth

Ans : Plot is not smooth


Q.13 What are the features of multivariate random variable?

       A. It is a set of unknown variables

       B. The variables have not occurred yet

       C. Both the options

Ans : Both the options


Q.14 What is posterior probability?

       A. The conditional probability of the event after the evidence is taken into consideration!

       B. The probability distribution that is done as a lack of evidence

       C. Both the options

       D. None of the options

Ans : Both the options


Q.15 Least number of coordinates required to showcase a point is ______________.

       A. Dimension

       B. Co-ordinates

       C. Multivariate analysis

       D. Bivariate analysis

Ans : Dimension


Q.16 Lurking variable remains _________________.

       A. Not defined

       B. Present during the analysis

       C. Omnipresent in the analysis

       D. Hidden during the analysis

Ans : Hidden during the analysis


Q.17 Which estimation can be represented by a single value?

       A. It is difficult to represent estimations as single value

       B. Interval estimation

       C. Point estimation

       D. All the options mentioned

Ans : All the options mentioned


Q.18 What is prior probability?

       A. Probability distribution done with a lack of evidence

       B. Probability distribution done with sufficient evidence

Ans : Probability distribution done with a lack of evidence


Q.19 What is density estimation?

       A. It estimates only probability

       B. It estimates probability density function

       C. It is a type of point estimation

       D. None of these

Ans : It is a type of point estimation


Q.20 What are the characteristics of Markov process?

       A. It is a Random process

       B. The past event does not affect the future event

       C. Both the options

Ans : Both the options


Q.21 In box kernel density estimation, __________.

       A. The histogram is centered over the data points

       B. The histogram is decentralized

       C. The histogram is decentralized over several data points

       D. None of the options

Ans : The histogram is centered over the data points


Q.22 Probability mass function is also known as ________.

       A. Mass function

       B. Probability density function

       C. Simple mass function

       D. Statistical mass function

Ans : Probability density function


Q.23 Principal component analysis reduces ____________.

       A. Large number of uncorelated variables

       B. Large number of corelated variables

       C. Both the options

Ans : Large number of corelated variables


Q.24 What is Kernel density estimation?

       A. It is the implementation of parametric density estimation

       B. It is the implementation of non – parametric density estimation

Ans : It is the implementation of non – parametric density estimation


Q.25 _____________ is an example of Multivariate analysis in which relationship exists between a dependent variable and independent variable/variables.

       A. Partial Least Squares Regression

       B. Cluster analysis

       C. None of the options

Ans : Partial Least Squares Regression


Q.26 What is data analysis?

       A. Organizing the data

       B. Analyzing the data

       C. Interpretation of the data

       D. All the options

Ans : All the options


Q.27 We use _______ in histogram for sub intervals.

       A. “blocks”

       B. “points”

       C. “segments”

       D. “bins”

Ans : “bins”


Q.28 What is Random walk?

       A. We can sometime predict the outcome in advance

       B. We can always predict the outcome in advance

       C. We cannot predict the outcome in advance

       D. None of the options

Ans : We can sometime predict the outcome in advance


Q.29 What is box kernel density estimate?

       A. Block in the histogram is averaged somewhere

       B. Blocks of the histogram are integrated

       C. Block in the histogram is centered over the data points

       D. Blocks of the histogram are combined to form the overall block

Ans : Block in the histogram is centered over the data points


Q.30 If time space or state space is discrete, ___________.

       A. Markov process can be termed as continuous-time Markov chains

       B. Markov process can be termed as discrete-time Markov chains

       C. None of the options

Ans : Markov process can be termed as discrete-time Markov chains


Q.31 p(x|?) is also known as the _________.

       A. Estimation

       B. Likelihood

       C. Probability

       D. Variance

Ans : Likelihood


Q.32 What is parameter?

       A. It tells something useful about the population!

       B. Parameter is a measurable quantity

       C. Both of these

       D. None of these

Ans : Parameter is a measurable quantity


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