6. Day I: Normal Distributions

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Presentation transcript:

6. Day I: Normal Distributions By the end of class you will be able to explain properties of a normal distribution.

We already saw distributions for Discrete Variables Variables have a finite # of values

Now: Continuous Variables Can take on an infinite # of values Examples???

Heights of Classmates What type of distribution do you think would represent the heights of everyone in this room? Draw a picture of what you think this distribution would look like.

What’s Not Normal??

Left-Skewed Distribution Also called Negatively Skewed Majority of data falls to right of mean Mean, Median, Mode What kind of data do you think could be represented with this distribution?

Right-Skewed Distribution Also called Positively Skewed Majority of data falls to the left of the mean Mode, Median, Mean What kind of data do you think could be represented by this type of distribution?

What’s Normal??

Symmetric Distributions Data values are evenly distributed around the mean What kind of data do you think could be represented by this distribution?

Properties of Normal Distribution: Bell Shaped Mean, Median and Mode are the same and located at the center Curve is unimodal Curve is symmetric about the mean Curve is continuous Curve never touches the x-axis, but it gets closer and closer to it

Properties of Normal Distribution (contd) Total area under the curve = 1, or 100% Area under curve follows empirical rule (SAT Scores)

The Equation:

ND: Same mean, different standard deviations:

ND: Different means, same standard deviation:

ND: different means and standard deviations:

Relay Summarizer Each person writes a different fact about normal distributions. Once you have four facts completely written out raise your paper.