Random Variable
A numerical description of the outcome of an experiment
Discrete Random Variable
A random variable that can assume only a finite number of values
Binomial Probability Distribution
A probability distribution showing the probability of X successes in n trials, where the probability of success does not change from trial to trial
A continuous random variable may assume
any value in an interval or collection of intervals
Probability Distribution
A description of the distribution of the values of a random variable and their associated probabilities
Expected Value
A weighted average of the value of a random variable, where the probability function provides weights
Discrete Random Variable
The number of customers that enter a store during one day is an example of..
The weight of an object is an example of
Continuous random variable
When sampling without replacement, the probability of obtaining a certain sample is best given by a
Hypergeometric distribution
The Poisson probability distribution is a
Discrete probability distribution
In the textile industry, a manufacturer is interested in the number of blemishes or flaws occuring in each 100 feet of material. The probability distribution that has the greatest chance of applying to this situation is the
Poisson Distribution
The expected value for a binomial probability distribution is
E(X)=nP
The key difference between the binomial and hypergeometric distribution is that with the hypergeometric distribution
the probability of success changes from trial to trial
A random variable that may take on any value in an interval or collection of intervals is known as a
Continuous random variable
The center of a normal curve is
the mean of the distribution
The probability that a continuous random variable takes any specific value is
is equal to zero
A normal distribution with a mean of 0 and a standard deviation of 1 is called
A standard normal distribution
The Z score for the standard normal distribution
can be either negative or positive
A negative value of Z indicates that
the number of standard deviations of an observation is to the left of the mean
The uniform, normal, and exponential distributions are
All continuous probability distributions
For a continuous random variable X, the probability density function F(x) represents
the height of the function at X
The Uniform probability distribution is used with
a continuos random variable
For the standard normal probability distribution, the area to the left of the mean is
0.5
For a uniform probability density function
the height of the function is the same for each value of X
A uniform probability distribution is a continuous probability distribution where the probability that the random variable assumes a value in any interval of equal lenght is
the same for each interval
When a continuous probability distribution is used to approximate a discrete probability distribution
a value of 0.5 is added or subtracted from the value of x
A continuous probability distribution that is useful in describing the time, or space, between occurrences of an event is a
Poisson probability distribution
An exponential probability distribution
is a continuous distribution
The ----- of a discrete probability distribution measures the spread of each outcome of the random variable from the mean of the distribution
standard deviation
The poisson probability distribution is a
Discrete probability distribution
The mean and the variance are equal for a ----- probability distribution
Poisson