The table below shows enrollment,† in millions of people, in private colleges in the United States during the years from 2004 through 2008. Date Enrollment in millions 2004 4.29 2005 4.47 2006 4.58 2007 4.76 2008 5.13 Find the equation of the regression line model for college enrollment as a function of time. (Let t be the number of years since 2004 and E the enrollment in private colleges, in millions. Round regression line parameters to two decimal places.

The table below shows enrollment,† in millions of people, in private colleges in the United States during the years from 2004 through 2008. Date Enrollment in millions 2004 4.29 2005 4.47 2006 4.58 2007 4.76 2008 5.13 Find the equation of the regression line model for college enrollment as a function of time. (Let t be the number of years since 2004 and E the enrollment in private colleges, in millions. Round regression line parameters to two decimal places.

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Date	Enrollment in millions
2004	4.29
2005	4.47
2006	4.58
2007	4.76
2008	5.13

Similar questions

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?
Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semi-fragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P14_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semi-fragile cargo.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.5 8.2 6.6 3.7 2.9 2.8 2.1 0.4 yy 13.6 11.2 9.3 7.2 6.7 6.4 6 4.3 xx = thousands of automatic weaponsyy = murders per 100,000 residents Use your calculator to determine the equation of the regression line. (Round to 2 decimal places)Determine the regression equation in y = ax + b form and write it below. A) How many murders per 100,000 residents can be expected in a state with 10.5 thousand automatic weapons?Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 10.8 thousand automatic weapons?Answer = Round to 3 decimal places.
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Use the general equation for the least square regression line to show that this line always passes through the point (x,y) * bars above the x and y.That is, set x=x(with a bar above the x) and show that the line predicts that y=y (with a bar above the y).
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Here is a bivariate data set. Find the regression equation for the response variable y. x y 60.9 36.2 55.8 44.3 27.8 61.7 36.1 56.3 56 53.7 49.8 55.2 45.7 51.5 60.1 46.6 63.1 50 63.9 40.2 68.8 45.4 32.3 60.4 76.6 40.6 65.4 46 65.6 42.7 regression equation: Enter the equation in slope-intercept form with parameters accurate to three decimal places.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.7 8 7.1 3.3 2.5 2.6 2.2 0.9 yy 14.3 10.5 10.4 6.7 6.1 6.2 6.1 4.7 xx = thousands of automatic weaponsyy = murders per 100,000 residents a) Use your calculator to determine the equation of the regression line. (Round to 2 decimal places)Determine the regression equation in y = ax + b form and write it below. ? B) How many murders per 100,000 residents can be expected in a state with 6.5 thousand automatic weapons ? Round to 3 decimal places.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.7 8 7.1 3.3 2.5 2.6 2.2 0.9 yy 14.3 10.5 10.4 6.7 6.1 6.2 6.1 4.7 xx = thousands of automatic weaponsyy = murders per 100,000 residents Use your calculator to determine the equation of the regression line. (Round to 2 decimal places)(A)Determine the regression equation in y = ax + b form and write it below?(B) How many murders per 100,000 residents can be expected in a state with 1.8 thousand automatic weapons? Answer = Round to 3 decimal places. (C) How many murders per 100,000 residents can be expected in a state with 6.5 thousand automatic weapons? Answer = Round to 3 decimal places.