Chapter 11

IN THIS CHAPTER

**Understanding linear and exponential models**

**Using SLOPE and INTERCEPT to describe linear data**

**Predicting future data from existing data**

**Working with normal and Poisson distributions**

When you’re analyzing data, one of the most important steps is usually to determine what model fits the data. No, I'm not talking about a model car or model plane! This is a *mathematical model* or, put another way, a *formula* that describes the data. The question of a model is applicable to all data that comes in X-Y pairs, such as the following:

- Comparisons of weight and height measurements
- Data on salary versus educational level
- Number of fish feeding in a river by time of day
- Number of employees calling in sick as related to day of the week

Suppose now that you plot all the data points on a chart — a *scatter chart,* in Excel terminology. What does the pattern look like? If the data is linear, the data points fall more or less along a straight line. If they fall along a curve rather than a straight line, they aren’t ...

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