Postage Cost Worksheet

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Student Name: ____Chesey Colston ______ Date: _____12/3/2014_____ Class: Algebra 2 Explore Linear Models: Years of School Average Weekly Paycheck Some high school 11 450 High school graduate 13 585 Some college 15 810 Bachelor’s degree 17 1000 Master’s degree 19 1250 Doctoral degree 21 1600 1) Which type of function (linear, exponential, or cubic) do you believe will best fit the data? Support your choice. My choice would be a Linear Function because it doesn’t go through an intersection like an exponential function would, it also wouldn’t be a cubic function because it is not curved. It has all straight lines. 2) Find the rate of change between the data points of these intervals: (High school graduate) and (Some college) High…show more content…
Describe the pattern you see in them. 4) Use exponential regression (or find a constant ratio) to determine an equation for the data. 5) Graph the equation you wrote in step four superimposed over the original data. Comment on how well or how poorly the equation fits the data. 6) If the rounds of forwarding continued in the same pattern, predict the round that would be first to end up sending the link to more than 50,000 people. Explain how you came up with this prediction. Explore Cubic Models: Record the volumes. Size of Corner Cut Volume of the Box (lwh) 1 100 2 3 4 5 Graph the table of data. Use your graph to answer the questions that follow. 1) Which type of function (linear, exponential, or cubic) do you believe will best fit the data? Support your choice. 2) Find the rate of change between the data points of these intervals: (Corner Cut 1) and (Corner Cut 2) (Corner Cut 2) and (Corner Cut 3) (Corner Cut 3) and (Corner Cut 4) 3) What do the rate of change values you just calculated represent? Why are some positive and some negative? 4) Use cubic regression to determine an equation for the data (or lwh where (12 – x) represents the sides and (x) represents the height of the box). 5) Graph the equation you wrote in step four superimposed over the original data. Comment on how well or how

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