1. Lesson 3.1: Normal Distribution

2. Normal Distribution
A bell-shaped probability distribution (shape) of data, that follows the following rules.
a) Symmetric, with a central peak (mean & median& mode are the same)
b) Follows the “68%, 95%, 99.7%” rule.
This distribution occurs a lot in both natural and man-made phenomena

3. Examples of Normal Distributions

4. Examples of Normal Distributions

5. Normal Distribution
The two “tails” of the graph will continue forever, however, if the data follows a normal distribution, the chances of getting a data value that far away from the mean is basically zero.
For distribution of discrete data, any probabilities can be calculated by counting all the possible outcomes.
For continuous distributions, we will calculate the area under the graph for the desired range of values (more on that in the next lessons).

6. Describing Distributions
Mound Shaped (symmetric)
Uniform

7. Describing Distributions
Negatively (Left) Skewed
Positively (Right) Skewed

8. Mean, Median and Mode in Skewed Distributions
Positively Skewed
Negatively Skewed

9. Bimodal Distribution
This distribution may occur when a population consists of two groups with different attributes.
i.e. Population shoe sizes can have 2 modes because men generally have larger feet than women.
test 3

10. Practice Questions Worksheet on my website: #1 – 3
Look at proposal #2 on my website and start working on it.