Chapter 6

the probability of that a normal random variable is less then its mean is

.5

the height of the probability density function f(x) of the uniform distribution defined on the interval (a,b) is

1/(b-a) between a and b, and 0 otherwise

How many parameters are needed to fully describe any normal distribution?

2

Which of the following is correct?

A continuous random variable has a probability DENISTY function, and a discrete random variable has a probability MASS function

the cumulative distribution function is denoted and defined by which of the following?

F(x) and F(x) = P(X<x)

A continuous random variable is characterized by uncountable values and can take on any value within an interval

True

which of the following is not a characteristic of a probability density function?

f(x) is symmetric around the mean

a continuous random variable has the uniform function on the interval (a,b) if its probability density function f(x)

is constant for all x between a and b, and 0 otherwise

the cumulative distribution function F(x) of a continuous random variable X with the probability density function f(x) is which of the following?

the area under f over all values that are x or less

which of the following does not represent a continuous random variable?

the number of customer arrivals to a bank BETWEEN 10am and 11am

what does it mean when we say that the tails of the normal curve are asymptotic to the x axis?

the tails get closer and closer to the x axis but never touch

the continuos uniform distribution describes a random variable, defined on the interval (a,b), that has an equally likely chance of assuming vales of within any subinterval of (a,b) with the same length

true

the mean of a continuous uniform distribution is simply the average of the upper and lower limits of the interval on which the interval is defined

true

the probability density function of a continuous random variable can be regarded as a counterpart of the probability mass function of a discrete variable

true

The letter Z is used to denote a random variable with any normal distribution

false

cumulative distribution functions can only be used to compute probabilities for continuous random variables

false

examples of random variables that closely follow a normal distribution include the age and the class year designation of a college student

false

given that the probability distribution is normal, it is completely described by its mean u>0 and its standard deviation o>0

false

just as in the case of the continuous uniform distribution, the probability density function of the normal distribution way be easily used to compute probabilities

false

the mean and standard deviation of the continuous uniform distribution are equal

false

the probability density function of a continuous uniform distribution is positive for all values between negative infinity and positive infinity

false

we are often interested in finding the probability that a continuous random variable assumes a particular value

false

the probability density function of a normal distribution is in general characterized by being symmetric and bell-shaped

true

the standard normal distribution is a normal distribution with mean equal to zero and standard deviation equal to one

true

the standard normal table is also referred to as the z table

true