Introduction to Probability Distributions

A binomial probability distribution requires:

• A. Independent trials.

• B. Trials with changing probabilities.

• C. More than two possible outcomes per trial.

• D. A variable interval of trials.

In a bivariate distribution, the covariance measures:

• A. The variability of a single random variable.

• B. The linear association between two random variables.

• C. The probability of one variable given another.

• D. The sum of two random variables.

What is the joint probability in a bivariate distribution?

• A. The probability of the sum of two variables.

• B. The probability of each individual event.

• C. The probability of two events occurring together.

• D. The difference between two probabilities.

A bivariate probability distribution involves:

• A. One random variable.

• B. Two random variables.

• C. Three random variables.

• D. An infinite number of random variables.

The probability distribution of a discrete random variable (x) is described by a:

• A. Cumulative distribution function.

• B. Density function.

• C. Probability function.

• D. Continuous function.

The marginal probabilities in a bivariate distribution are:

• A. The probabilities of the joint occurrence of two events.

• B. The probabilities of each individual event.

• C. Always equal to the mean.

• D. The difference between two probabilities.

Which function in Excel would you use to compute hypergeometric probabilities?

• A. Binom.Dist

• B. Poisson.Dist

• C. Hypergeom.Dist

• D. Norm.Dist

In financial applications, bivariate distributions can be used to:

• A. Measure the linear association between different financial variables.

• B. Calculate the median of a financial variable.

• C. Determine the mode of a financial variable.

• D. Compute the standard deviation of a single variable.

Which of the following distributions is used to model the number of times an event occurs in a fixed interval of time or space?

• A. Binomial distribution.

• B. Poisson distribution.

• C. Hypergeometric distribution.

• D. Uniform distribution.

The variance of a random variable is:

• A. The positive square root of the standard deviation.

• B. A measure of the central location of the random variable.

• C. The average of the squared differences from the mean.

• D. The same as the standard deviation.