Let Y represent the profit (or loss) for a certaincompany X years after 1975. Based on the datashown below, a statistician calculates a linear model0.68X+21.75.Y=X5678910111213141516171819yy =2016.451616.3514.715.3514.914.2512.513.5511.210.6511.210.657Use the model to estimate the profit in 1993.

We have given model Y=-0.68X+21.75 To get profit in 1993 Put, X=1993-1975 =18 So, Y=-0.68×18+21.75…

Answered: Let Y represent the profit (or loss)…

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter5: Inverse, Exponential, And Logarithmic Functions

Section5.3: The Natural Exponential Function

Problem 44E

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The ordered pairs below give the median sales prices y (in thousands of dollars) of new homes sold in a neighborhood from 2009 through 2016. (2009, 179.4) (2011, 191.0) (2013, 202.6) (2015, 214.9) (2010, 185.4) (2012, 196.7) (2014, 208.7) (2016, 221.4) A linear model that approximates the data is y=5.96t+125.5,9t16, where t represents the year, with t=9 corresponding to 2009. Plot the actual data and the model on the same graph. How closely does the model represent the data?
For the following exercises, consider this scenario: The profit of a company decreased steadily overa ten-year spam.The following ordered pairs shows dollars and the number of units sold in hundreds and the profit in thousands ofover the ten-year span, (number of units sold, profit) for specific recorded years: (46,600),(48,550),(50,505),(52,540),(54,495). Use linear regression to determine a function Pwhere the profit in thousands of dollars depends onthe number of units sold in hundreds.
Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?

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