# Postage Cost Worksheet Topics: Regression analysis, Derivative, Polynomial / Pages: 7 (657 words) / Published: Dec 15th, 2014

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
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 school graduate~ {585, 13} and some collage~ {810, 15}
M= {15-13} / {810-585}
M= 2 / 225
M= 112.5
(Some college) and (Bachelor’s degree)
Some Collage~ {810, 15} and Bachelor’s Degree~ {1000, 17}
M= {17-15} / {1000-810}
M= 2 / 190
M= 95

(Bachelor’s degree) and (Master’s degree)
Bachelor’s {1000, 17} and Masters {1250, 19}
M= {19-17} / {1250-1000}
M= 2 / 50
M= 125
3) Use linear regression (least squares or line of best-fit) to determine an equation for the data.

4) Compare the slope of the equation you just wrote with the rate of change values you calculated in step two. How are they the same or different? What do they represent?

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) Predict the amount of money you could expect to make each week based on 23 years of completed schooling. Explain how you came up with this prediction.

Explore Exponential Models:
Graph the table of data. Use your graph to answer the questions that follow.
Number of Rounds
People Receiving Forward
1
2
2
4
3

4

5

6

7

8

9

10

1) Which type of function (linear, exponential, or cubic) do you believe will best fit the data? Support your