Continuous Probability Distribution Quiz 2

This Post contains MCQS on continuous probability distribution quiz. It covers events, experiments, mutually exclusive events, collectively exhaustive events, sure events, impossible events, addition and multiplication laws of probability, concepts related to discrete and continuous random variables, probability distribution and probability density functions, characteristics and properties of probability distributions, discrete probability distribution, continuous probability distributions, etc. Let us start with Online MCQs on the continuous Probability Distribution Quiz.

MCQs on Continuous Probability Distribution Quiz

• 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 increases.
• 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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