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Math 243 Sections 6.1-6.2 The Normal Model
Here are some roughly symmetric, unimodal histograms
The Normal Model – The famous bell curve
Example 1. Let’s say the mean annual rainfall in Portland is 40 inches with a standard deviation of 10 inches. We need to assume the distribution is symmetric and unimodal in order to use the Normal model. Label the Normal model for this situation.
N(40,10)
68-95-99.7% Rule
In the normal model, about 68% of the values fall within 1 standard deviation of the mean, about 95% fall within 2 standard deviations of the mean, and about 99.7% fall within 3 standard deviations of the mean. This is also called the Empirical Rule. Label the bell curve above to show these key features.
A value that is more than two standard deviations away from the mean is considered unusual or an outlier. A value that is more than three standard deviations away from the mean is very rare.
Finding Probabilities using the Empirical Rule
Example 1 continued. To find a percentage using the Normal model, draw and label the model and shade the area or percentage that you want to find. In this case our mean annual rainfall in Portland is 40 inches with a standard deviation of 10 inches.
N(40,10)
a. What percentage of the time is the annual rainfall between 30 and 50 inches?
b. What is the probability that the annual rainfall is more than 50 inches?
c. What percentage of the time is the annual rainfall 20 inches or less?
d. What is the probability that the annual rainfall is 60 inches or less?
Finding Probabilities using GeoGebra
View > Probability Calculator > Select Normal in the dropdown menu under the graph
Type in the values for μ (the mean) and σ (the standard deviation)
Finding Normal Probabilities on GeoGebra
Select ] for less than, [ ] for between two values, and [ for greater than.
Example 2. Let’s continue the rainfall example where the mean annual rainfall in Portland is 40 inches with a standard deviation of 10 inches.
Find the cutoff values for:
a. The lowest 10% of rainfall (the 10 th^ percentile).
b. The highest 5% of rainfall (the 95 th^ percentile).
c. The middle 50% of rainfall.
Finding Inverse Normal Probabilities on GeoGebra
Type in the values for μ (the mean) and σ (the standard deviation)
Select ] for less than or [ for greater than. You can type in a probability and GeoGebra will calculate the cutoff value.
Example 3. Entry to a certain university is determined by a national test. The scores on this test are normally distributed with a mean of 50 points and a standard deviation of 15 points. Tom wants to be admitted to this university and he knows that he must score better than at least 70% of the students who took the test. What is the cutoff score for entrance to the university?
The Standard Normal Model
Now we want to compare unlike events: Even if two events are quite different, we can still compare them using the standard deviation as a ruler. We can see how many standard deviations each event is from its mean.
Example 4. Assume the average annual rainfall for in Portland is 40 inches per year with a standard deviation of 10 inches. Also assume that the average wind speed in Chicago is 10 mph with a standard deviation of 2 mph. Suppose that one year Portland's annual rainfall was only 10 inches and Chicago's average wind speed was 13 mph. Which of these events was more extraordinary?
Z-score Formulas:
For sample data: 𝑧𝑧 =
For population parameters: 𝑧𝑧 =
z-score for 10 inches of rain in Portland: z-score for a wind speed of 13 mph in Chicago:
Standard Normal, N(0,1)
Normal Approximations for Binomial Probability Models (Supplement Packet 6.1)
It is often more convenient to use the Normal model than the Binomial model. What do you notice about the two models?
Example 6. In GeoGebra, enter the Binomial model for the 20 coin flips, B(20, .5). Click on the “Overlay Normal Curve” Icon in the upper left corner. What do you think would happen to the histogram and the Normal curve if we increased the number of flips?
Change the number of flips to 50, 100, 200, and then 1000. What happens to the histogram and the Normal curve?
Example 7. Now let’s say an archer is shooting 2 arrows with a 90% chance of hitting the bull’s-eye. Enter this Binomial model and overlay the Normal curve. How does the Normal curve fit the histogram?
Try increasing the number of arrows shot. When would you feel comfortable using the Normal distribution to approximate the Binomial model?
Success/Failure Condition
A Binomial model is approximately Normal if we expect at least 10 successes and 10 failures, determined by: 𝑛𝑛𝑛𝑛 ≥ 10 and 𝑛𝑛𝑛𝑛 ≥ 10
Note: We MUST show that we’ve verified this condition every time we approximate a Binomial model
with a Normal model.
Example 8. A basketball player makes about 82% of her free throws. Assume each shot is independent of the last. She shoots 200 free throws.
a. Write down the Binomial model for this scenario.
b. Using the Binomial model, what is the probability that she will make at least 175 of the 200 free throws?
c. Check the success/failure condition to see whether we can approximate this distribution with a Normal model.
d. Then write down the Normal approximation model for this scenario. What are the mean and standard deviation?
e. Use the Normal approximation model to calculate the likelihood that this basketball player makes at least 175 out of 200 baskets. How do the two values compare?
