Stitz-Zeager_College_Algebra_e-book

# We will label the y intercepts of the ellipse as 0 b

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Unformatted text preview: what appears to be a diﬀerent model N = 269.414(1.22333419)t . Using properties of exponents, we write t e0.202t = e0.202 ≈ (1.223848)t , which, had we carried more decimal places, would have matched the base of the calculator model exactly. The exponential model didn’t ﬁt the data as well as the quadratic or power function model, but it stands to reason that, perhaps, the spread of the ﬂu is not unlike that of the spread of a rumor 390 Exponential and Logarithmic Functions and that a logistic model can be used to model the data. The calculator does have a ‘Logistic 10739 147 Regression’ feature, and using it produces the model N = 1+42.416.e0.268t . This appears to be an excellent ﬁt, but there is no friendly coeﬃcient of determination, R2 , by which to judge this numerically. There are good reasons for this, but they are far beyond the scope of the text. Which of the models, quadratic, power, exponential, or logistic is the ‘best model’ ? If by ‘best’ we mean ‘ﬁts closest to the data,...
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## This note was uploaded on 05/03/2013 for the course MATH Algebra taught by Professor Wong during the Fall '13 term at Chicago Academy High School.

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