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4.1 Transforming Data

4.1 Transforming Data. Pg. 195-210. Reexpressing . Applying a function such as the logarithm or square root to a quantitative transforming variable is called transforming or reexpressing the data. . Nonlinear Relationships.

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4.1 Transforming Data

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  1. 4.1 Transforming Data Pg. 195-210

  2. Reexpressing • Applying a function such as the logarithm or square root to a quantitative transforming variable is called transforming or reexpressing the data.

  3. Nonlinear Relationships • Data that displays a curved pattern can be modeled by a number of different functions.

  4. Most common nonlinear models • Exponential • Power • Our goal is to fit a model to curved data so that we can make predictions as we did in Chapter 3.

  5. Reintroduce Logs • Example: • Understood to be “base 10” so • See page 206 and website for properties of logarithms

  6. “Straighten it out” • The primary statistical tool we have to fit a model is the least-squares regression model. • Consider the exponential model • The essential property of the logarithm for our purposes is that it straightens an exponential growth curve. If a variable grows exponentially, its logarithm grows linearly.

  7. Assignment • Pg. 212 #4.6, 4.11

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