The following chart shows the total annual support for the arts in a country by federal, state, and local government in 1995-2003 as a function of time in years (t = 0 represents 1995) together with the regression line.Government funding for the ArtsFunding ($ billion)2.92.11.304.0 -0.07199524Year (1)(a) Over the period [0, 4] the average rate of change of government funding for the arts was the following.less than the rate predicted by the regression line.greater than the rate predicted by the regression line.O approximately equal to the rate predicted by the regression line.82003(b) Over the period [4, 8] the average rate of change of government funding for the arts was the following.O less than the rate predicted by the regression line.O greater than the rate predicted by the regression line.O approximately equal to the rate predicted by the regression line.(c) Over the period [3, 6] the average rate of change of government funding for the arts was the following.less than the rate predicted by the regression line.greater than the rate predicted by the regression line.O approximately equal to the rate predicted by the regression line.(d) Estimate, to two significant figures, the average rate of change of government funding for the arts over the period [0, 8]. (Be careful to state the units of measurement.)X billion dollars per yearHow does it compare to the slope of the regression line?O less than the slope of the regression lineO greater than the slope of the regression lineapproximately equal to the slope of the regression line

The chart indicates the total annual support for arts in the year 1995- 2003.

Answered: (d) Estimate, to two significant…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y-ax+b a=-1,263 b=21.291 12-0.5776 r=-0.76 Use this to predict the number of situps a person who watches 5 hours of TV can do (to one decimal place)
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.773 b=24.3 r2=0.5776 r=-0.76 Use this to predict the number of situps a person who watches 10 hours of TV can do (to one decimal place)
r = -0.579 Calculate the coefficient of determination. (Round to three decimal places as needed.) What does this tell you about the explained variation of the data about the regression line? % of the variation can be explained by the regression line. (Round to one decimal place as needed.) About the unexplained variation? % of the variation is unexplained and is due to other factors or to sampling error. (Round to one decimal place as needed.)
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A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.328 b=37.715 r?=0.788544 r=-0.888 Use this to predict the number of situps a person who watches 0.5 hours of TV can do (to one decimal place)
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An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
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The slope, b represents * O predicted value of Y when X = 0. O the estimated average change in Y per unit change in X. O variation around the line of regression. O the predicted value of Y.

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