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Published byMyrtle Kelly Carpenter Modified over 9 years ago
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4.1 Modeling Nonlinear Data
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Create scatter plots of non linear data Transform nonlinear data to use for prediction
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Exponential Function: Power function:
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To show exponential growth: ◦ We look for a common ratio ◦ Notice the common ratio is about 3! xyratio 13 28.78.7/3=2.9 326.926.9/8.7= 3.09 482.682.6/26.9 =3.07 52402.91
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Linear- increases by a constant (slope) Exponential- increases by a ratio
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The following table shows the heights of a Pasfor tree after 5 months. Graph age vs. height. (L1 vs. L2) Notice the graph shows an exponential growth model xy 13 28.7 326.9 482.6 5240
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Remember, we can’t find correlation or a regression line unless the data is linear. So how do we do this? Take the logarithm of y. In L3= log (L2) Now graph (x,log y)= (L1, L3) What do you notice? The data is linear!!! So now we can use it to predict! (L1) x (L2) y(L3) log y 13 28.7 326.9 482.6 5240
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If a variable grows exponentially, its logarithm grows linearally. ** this question will be a multiple choice on your test. For example: The oil production per year shows an exponential increase in productivity. How would you predict data using this model? A) Graph the year versus oil production B) Graph the logarithm of year versus oil production C) Graph the year versus the logarithm of oil production D) Graph the logarithm of year versus the logarithm of oil production E)We can’t predict data of exponential growth. Answer: C) Graph the year versus the logarithm of oil production
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The following data is a power function. When does a power law become linear? Take the log x and log y in L3 and L4 Then graph L3, L4 :(log x, log y) What do you notice? It’s linear!! lengthweight 12 217 353.1 4129.2 5248.7 L1L2L3 (Log L1) L4 (log L2) 12 217 353.1 4129.2 5248.7
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What do we need to actually know from section 4.1? If data grows exponentially- graph (x, log y) If data grows to a power function- graph ( log x, log y) That is it!!!! So don’t stress too much about this section-if you know these 2 facts, you are good!
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