Important MCQ Random Variables 1

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 The post is about MCQ Random Variables. There are 20 multiple-choice questions related to random experiments, random variables and types of random variables, expectations, discrete random variables, and continuous random variables. Let us start with the MCQ Random Variables Quiz.

Online MCQs about Random Variable with Answers

1. If $X$ is a random variable and $a$ and $b$ are constants then $Var(aX+ b)$ is equal to

 
 
 
 

2. A continuous random variable is a random variable that can

 
 
 
 

3. If $X$ is a continuous random variable, then function $f(X)$ is

 
 
 
 

4. If $C$ is a non-random variable, the $E(C)$ is

 
 
 
 

5. A continuous variable is a variable that can assume

 
 
 
 

6. If $X$ is a random variable that can take only non-negative values, then

 
 
 
 

7. Two random variables $X$ and $Y$ are said to be independent if:

 
 
 
 

8. If $X$ and $Y$ are independent random variables then $E(XY)$ is equal to

 
 
 
 
 

9. If $X$ and $Y$ are random variable then $E(X + Y)$ is equal to

 
 
 
 

10. If $X$ and $Y$ are two independent variables, then

 
 
 
 

11. Which of the following are examples of discrete random variables?

 
 
 
 

12. Which of the following statements accurately describes a key difference between discrete and continuous random variables?

 
 
 
 

13. A _____ random variable has a countable number of possible values.

 
 
 
 

14. A variable whose value is determined by the outcome of a random experiment is called

 
 
 
 
 

15. For a random variable $X$, $E(X)$ is

 
 
 
 

16. A variable (Random Variable) assuming a finite number of values is called

 
 
 
 

17. If $X$ is a discrete random variable, the function $f(X)$ is

 
 
 
 

18. When four coins are tossed, the value of a random variable (Numbers of head) is

 
 
 
 

19. Which of the following statements describes continuous random variables?

 
 
 
 

20. A variable (Random Variable) assuming an infinite number of values is called

 
 
 
 

MCQ Random Variables Quiz

MCQ Random Variables Quiz
  •  If $X$ is a continuous random variable, then function $f(X)$ is
  • A variable (Random Variable) assuming an infinite number of values is called
  • A variable whose value is determined by the outcome of a random experiment is called
  • If $X$ and $Y$ are random variable then $E(X + Y)$ is equal to
  • If $X$ is a discrete random variable, the function $f(X)$ is
  • When four coins are tossed, the value of a random variable (Numbers of head) is
  • A variable (Random Variable) assuming a finite number of values is called
  • If $X$ and $Y$ are independent random variables then $E(XY)$ is equal to
  • Two random variables $X$ and $Y$ are said to be independent if:
  • If $X$ and $Y$ are two independent variables, then
  • A continuous random variable is a random variable that can
  • If $X$ is a random variable that can take only non-negative values, then
  • For a random variable $X$, $E(X)$ is
  • If $C$ is a non-random variable, the $E(C)$ is
  • A continuous variable is a variable that can assume
  • A ———– random variable has a countable number of possible values.
  • Which of the following statements accurately describes a key difference between discrete and continuous random variables?
  • Which of the following are examples of discrete random variables?
  • Which of the following statements describes continuous random variables?
  • If $X$ is a random variable and $a$ and $b$ are constants then $Var(aX+ b)$ is equal to
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Important MCQs Probability Distributions 4

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The post is about MCQs Probability Distributions. There are 20 multiple-choice questions covering the topics related to Chi-Square distribution, F-distribution, Binomial distribution, Student’s t distribution, and properties of distributions. Let us start with MCQs Probability Distributions.

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MCQs Probability Distributions Quiz

MCQs Probability Distributions with Answers
  • A family of parametric distribution in which mean always greater than its variance is:
  • The family of parametric distributions which has a mean always less than variance is:
  • The family of parametric distributions for which moment generating function does not exist is:
  • The distribution for which the mode does not exist is:
  • The relation between the mean and variance of $\chi^2$ with $n$ degrees of freedom is
  • The $F$-distribution curve in respect of tails is:
  • If $X$ has a binomial distribution with parameter $p$ and $n$ then $\frac{X}{n}$ has the variance:
  • The binomial distribution is symmetrical if $p=p=?$
  • The shape of geometric distribution is
  • If $X\sim N(\mu, \sigma^2)$ and $a$ and $b$ are real numbers, then mean of $(aX+b)$ is
  • The distribution of sample correlation is
  • Events having an equal chance of occurrence are called
  • Student’s $t$-distribution curve is symmetrical about mean, it means that
  • The distribution possessing the memoryless property is
  • The chi-square distribution is used for the test of
  • The probability of failure in binomial distribution is denoted by
  • In binomial distribution, the formula for calculating the mean is  
  • In binomial probability distribution, dependents of standard deviations must include
  • The formula to calculate standardized normal random variables is
  • The mean of a binomial probability distribution is 857.6 and the probability is 64% then the number of values of binomial distribution
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Best Probability Distribution Questions 3

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The post is about Probability Distribution Questions. There are 20 multiple-choice questions covering topics related to normal probability distribution, standard normal probability distribution, and its properties. Let us start with the Quiz Probability Distribution Questions.

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Probability Distribution Questions with Answers

MCQs Probability Distribution Questions with Answers
  • The area under the normal curve on either side of the mean is
  • In the case of a symmetrical distribution
  • The mean deviation of Normal Distribution is
  • The Normal Distribution has parameters
  • In Normal distribution, the parameters which control the flatness of the curve is
  • We use normal distribution when $n$ is
  • The median of the normal distribution corresponds to the value of $Z$ equal to
  • The lower and upper quartiles of standard normal variate are respectively
  • The shape of the normal curve can be related to
  • The total Area under the normal curve is
  • Which of the following parameters controls the relative flatness of a normal distribution
  • In a normal distribution $E(X−\mu)^2$ is
  • If $X\sim N(55,49)$ then $\sigma$
  • The Normal Curve is asymptotic to the
  • The shape of the normal curve depends upon
  • If $X\sim N(16, 49)$, then mean is
  • Normal Distribution is
  • If $Y=5X + 10$ and $X$ is $N(10,25)$, then mean of $Y$ is
  • Normal Distribution is
  • The formula in which binomial distribution approaches normal probability distribution with the help of normal variable is written as
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Important MCQs Continuous Probability Distribution Quiz 2

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The post is about Continuous Probability Distribution Quiz. There are 20 multiple-choice questions. The quiz covers topics related to the distribution function, area under the curve, density function, binomial, geometric, and poison probability discrete distribution. Let us start with MCQs Continuous Probability Distribution Quiz.

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MCQs Continuous Probability Distribution Quiz

MCQs continuous Probability Distribution Quiz with Answers
  • A discrete probability distribution may be represented by
  • The total Area under the curve in the probability of density function is
  • The distribution function $F(X)$ is represented by
  • The probability function is always
  • For distribution Function $F(X)$, $F(−\infty)=0$ and $F(\infty) = ?$
  • For a probability density function (PDF), the probability of a single point is
  • The probability distribution of a random variable is also known as
  • A continuous probability distribution can be represented by
  • A discrete probability distribution may be represented by
  • The central limit theorem states that the sampling distribution of the mean approaches a ——— distribution as the sample size increase.
  • The Poisson distribution can model which of the following kinds of data? Select all that apply.
  • The binomial distribution models the probability of events with ——— possible outcomes.
  • The Poisson distribution can model the probability that a certain number of events will occur during a specific time period.
  • What probability distribution represents experiments with repeated trials that each have two possible outcomes: success or failure?
  • At what sample size does the t-distribution become practically the same as the normal distribution?
  • What shape is the graph of the t-distribution?
  • If $X$ has a normal distribution with mean $\mu$ and variance $\sigma^2$, then $y=\frac{(X-\mu)^2}{\sigma^2}$
  • The binomial distribution is skewed right when
  • In what case would the Poisson distribution be a good approximation of the binomial distribution:
  • The mode of the geometric distribution is:
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