The graph of a normal distribution is the (a)   .

The (a)   is bell-shaped and is symmetric about the mean.

In a (a)   , the mean, median, and mode are equal.

The total area under the normal curve is equal to (a)   .

The (a)   approaches, but never touches, the x-axis as it extends farther and farther away from the mean.

The points at which the curve changes from curving upward to curving downward are called (a)   .

State the two requirements for a probability density functioin.

Because every normal distribution can be transformed to the standard normal distribution, you can use z-scores and the standard normal curve to find area, and therefore (a)   , under the normal curve.

The horizontal scale of the graph of the standard normal distribution corresponds to (a)   .

A (a)   is a measure of position that indicates the number of standard deviations a value lies from the mean.

The (a)   is a normal distribution with a mean of 0 and a standard deviation of 1.

The random variable x is sometimes called a (a)   and represents the valuies in a nonstandard normal distribution.

The (a)   lists the cumulative area under the standard normal curve to the left of z-scores from -3.49 to 3.49.

The cumulative area is close to 0 for z-scores close to z = (a)   .

The cumulative area (a)   as the z-scores increase.

The cumulative area for z = 0 is (a)   .

The cumulative area is close to 1 for z-scores close to z = (a)   .

When the z-score is not in the table, use the entry (a)   .

Values lying more than two standard deviations from the mean are considered (a)   .

Values lying more than three standard deviations from the mean are considered (a)   .

If a z-score is greater than 2 or less than -2, it is (a)   .

If a z-score is greater than 3 or less than -3, it is (a)   .

If a random variable x is (a)   , you can find the probability that x will fall in a given interval by calculating the area under the normal curve for the given interval.

(a)   divide a data set into 100 equal parts.

If the given area is halfway between two area entries, use the z-score (a)   between the corresponding z-scores.

A (a)   is the probability distribution of a sample statistic that is formed when samples of size n are repeatedly taken from a population.

Sample means can vary from one another and can also vary from the population mean. This type of variation is to be expected and is called (a)   .

The standard deviation of the sampling distribution of the sample means is called the (a)   .

The (a)   forms the foundation for the inferential branch of statistics which describes the relationship between the sampling distribution of sample means and the population that the samples are taken from.

The (a)   is an important tool that provides the information you'll need to use sample statistics to make inferences about a population mean.

An (a)   is one that occurs with a probability of less than 5%.

The (a)   can also be used to investigate unusual events.

A binomial distribution is discrete and can be represented by a (a)   histogram.

When you use a continuous normal distribution to approximate a binomial probability, you need to move 0.5 unit to the left and right of the midpoint to include all possible x-values in the interval. When you do this, you are making a (a)   .

To use a (a)   , simply subtract 0.5 from the lowest value and/or add 0.5 to the highest.

In a (a)   there is a difference such that the probability that x is exactly c is not 0.

In a (a)   there is no difference because the probability that x is eaxctly c is 0.

It is a probability distribution wherein its random variables have infinite number of possible values that can be represented by an interval on the number line.

(a)