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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 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 • Data that displays a curved pattern can be modeled by a number of different functions.
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.
Reintroduce Logs • Example: • Understood to be “base 10” so • See page 206 and website for properties of logarithms
“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.
Assignment • Pg. 212 #4.6, 4.11