<img src="https://d5nxst8fruw4z.cloudfront.net/atrk.gif?account=iA1Pi1a8Dy00ym" style="display:none" height="1" width="1" alt="" />

# Applications of Normal Distributions

## Using computational skill and technology to sketch and shade appropriate area under the normal curve

Estimated11 minsto complete
%
Progress
Practice Applications of Normal Distributions
Progress
Estimated11 minsto complete
%
Area Under Density Curve of Normal Distribution

A normal density curve is an idealized representation of a normal distribution in which the area under the curve is defined to be 1.

• The points at which the curve changes from being concave up to being concave down are called the inflection points
• On a normal density curve, these inflection points are always exactly one standard deviation away from the mean

The Empirical Rule for Normal Distributions

One way to calculate the area under a density curve up to a certain point is by using the Empirical Rule.

The Empirical Rule states that

• 50% of all data points are above the mean and 50% are below
• Approximately 68% of all data points are within 1 standard deviation of the mean
• Approximately 95% of all data points are within 2 standard deviations of the mean
• Approximately 99.7% of all data points are within 3 standard deviations of the mean

[Figure1]

Z-Score

If the data point you are interested in is not a whole number standard deviation away from the mean, you use a z-score.

A z-score measures how many standard deviations a score is away from the mean.  The z-score of the term x\begin{align*}x\end{align*} in a population distribution whose mean is μ\begin{align*}\mu\end{align*} and whose standard deviation is σ\begin{align*}\sigma\end{align*} is given by: z=xμσ\begin{align*}z=\frac{x-\mu}{\sigma}\end{align*} .

Use a z-score probability table to find a decimal expression of the percentage of values that are less than x\begin{align*}x\end{align*}.