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AP Statistics Chapter 2
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Terms in this set (11)
The 68-95-99.7 Rule
Also known as the "empirical rule." In the Normal distribution
with mean μ and standard deviation σ, (a) Approximately 68% of the observations fall
within σ of the mean μ, (b) Approximately 95% of the observations fall within 2σ of μ,
and (c) Approximately 99.7% of the observations fall within 3σ of μ.
Cumulative relative frequency graph
The completed graph shows the accumulating percent of observations as
you move through the classes in increasing order.
Density curve
A curve that (a) is always on or above the horizontal axis, and (b) has
exactly 1 area underneath it. A density curve describes the overall pattern of a
distribution. The area under the curve and above any interval of values on the horizontal
axis is the proportion of all observations that fall in that interval.
Mean of a density curve
The point at which the curve would balance if made of solid
material.
Median of a density curve
The point with half the area under the curve to its left and
the remaining half of the area to its right.
Normal curves
An important class of density curves that are symmetric, single-peaked,
and bell-shaped.
Normal distribution
Used to assess whether a data set follows a Normal
distribution. To make a Normal probability plot, (1) arrange the data values from
smallest to largest and record the percentile of each observation, (2) use the standard
Normal distribution to find the z-scores at these same percentiles, and (3) plot each
observation x against the corresponding z. If the points on a Normal probability plot lie
close to a straight line, the plot indicates that the data are approximately Normal.
Pth percentile
The value with p percent of the observations less than it.
Standard Normal distribution
The Normal distribution with mean 0 and standard
deviation 1.
Standard Normal table (Table A)
A table of areas under the standard Normal curve.
The table entry for each value z is the area under the curve to the left of z.
Standardized values (z-scores)
If x is an observation from a distribution that has known mean and standard deviation, the standardized value of x z = (x-mean)/S.d.
standardized value is often called a z-score
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