The sample average is a random variable and

The sample average is a random variable and

A) is a single number and as a result cannot have a distribution.
B) has a probability distribution called its sampling distribution.
C) has a probability distribution called the standard normal distribution.
D) has a probability distribution that is the same as for the Y_1,... Y_n i.i.d. variables.

Answer: B

If variables with a multivariate normal distribution have covariances that equal zero, then

If variables with a multivariate normal distribution have covariances that equal zero, then

A) the correlation will most often be zero, but does not have to be.
B) the variables are independent.
C) you should use the distribution to calculate probabilities.
D) the marginal distribution of each of the variables is no longer normal.

Answer: B

Assume that Y is normally distributed N(miu, sigma) . Moving from the mean (miu) 1.96 standard deviations to the left and 1.96 standard deviations to the right, then the area under the normal p.d.f. is

Assume that Y is normally distributed N(miu, sigma) . Moving from the mean (miu) 1.96 standard
deviations to the left and 1.96 standard deviations to the right, then the area under the normal p.d.f.
is

A) 0.67
B) 0.05
C) 0.95
D) 0.33


Answer: C

To standardize a variable you

To standardize a variable you

A) subtract its mean and divide by its standard deviation.
B) integrate the area below two points under the normal distribution.
C) add and subtract 1.96 times the standard deviation to the variable.
D) divide it by its standard deviation, as long as its mean is 1.

Answer: A

The correlation between X and Y

The correlation between X and Y

A) cannot be negative since variances are always positive.
B) is the covariance squared.
C) can be calculated by dividing the covariance between X and Y by the product of the two
standard deviations.
D) is given by corr(X,Y) = COV(X,Y)/[VAR(X)*Var(Y)]

Answer: C

For a normal distribution, the skewness and kurtosis measures are as follows:

For a normal distribution, the skewness and kurtosis measures are as follows:

A) 1.96 and 4
B) 0 and 0
C) 0 and 3
D) 1 and 2

Answer: C

The expected value of a discrete random variable

The expected value of a discrete random variable

A) is the outcome that is most likely to occur.
B) can be found by determining the 50% value in the c.d.f.
C) equals the population median.
D) is computed as a weighted average of the possible outcome of that random variable, where
the weights are the probabilities of that outcome.

Answer: D

The cumulative probability distribution shows the probability

The cumulative probability distribution shows the probability

A) that a random variable is less than or equal to a particular value.
B) of two or more events occurring at once.
C) of all possible events occurring.
D) that a random variable takes on a particular value given that another event has happened.

Answer: A

The probability of an event A or B (Pr(A or B)) to occur equals

The probability of an event A or B (Pr(A or B)) to occur equals

A) Pr(A) x Pr(B)
B) Pr(A) + Pr(B) if A and B are mutually exclusive.
C) Pr(A)/Pr(B)
D) Pr(A) + Pr(B) even if A and B are not mutually exclusive.

Answer: B

The probability of an outcome

The probability of an outcome

A) is the number of times that the outcome occurs in the long run.
B) equals M N, where M is the number of occurrences and N is the population size.
C) is the proportion of times that the outcome occurs in the long run.
D) equals the sample mean divided by the sample standard deviation.

Answer: C