Many traditional textbook problems begin with an equation and then produce the graph of the equation. In many real-world problems, researchers first gather and graph data, then attempt to find an equation that fits the data. The shape of the data determines the type of equation that best fits the data. The equation can then be used to predict values not in the data set. Technology facilitates this real-world approach to functions and graphs.

In this module you will learn to use the TI-83 to create a scatter plot of data that can be modeled by a

Logistic functions are used to represent growth that has a limiting factor, such as food supplies, war, new diseases, etc. Logistic models are often used to model population growth or the spread of disease or rumor.

logistic function. Then a

An equation that represents a set of data is called a regression equation, and it is used to estimate or predict additional values not in the data set.

regression equation that fits the data can be found.

Lesson Index:

5.1 - Simulating Logistic Growth with The Spread-of-a-Rumor Experiment

5.2 - Modeling Logistic Growth

5.3 - Change in y

After completing this module, you should be able to do the following:

• Simulate a logistic growth problem
• Find and graph a logistic regression equation to fit a data set
• Understand the logistic curve
• Calculate and graph the changes in y
• Describe what the changes in y tell you about the original data