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state afferent times. Typically, what the federal government finds is that "poor" states tend to stay poor and "wealthy" states tend to stay wealthy. Would we have been able to predict the 1999 per capita income for a state (denoted by») from its 1980 per capita income (denoted by x)? The following bivariate data give the per capita income (in thousands of dollars) for a sample of states in the years 1980 and 1999 (source: U.S. Bureau of Economic Analysis, Survey of Current Business, May 2000). The data are plotted in the scatter plot below, and the least-squares regression line is drawn. The equation for this line is -8.85 +1.87x. Montana Missouri 1980 per capita income, x (in $1000s) 9.1 9.4 1999 per capita income, y (in $1000s) 22.3 26.2

state afferent times. Typically, what the federal government finds is that "poor" states tend to stay poor and "wealthy" states tend to stay wealthy. Would we have been able to predict the 1999 per capita income for a state (denoted by») from its 1980 per capita income (denoted by x)? The following bivariate data give the per capita income (in thousands of dollars) for a sample of states in the years 1980 and 1999 (source: U.S. Bureau of Economic Analysis, Survey of Current Business, May 2000). The data are plotted in the scatter plot below, and the least-squares regression line is drawn. The equation for this line is -8.85 +1.87x. Montana Missouri 1980 per capita income, x (in $1000s) 9.1 9.4 1999 per capita income, y (in $1000s) 22.3 26.2

MATLAB: An Introduction with Applications
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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Regression and Correlation TOPIC NAME Predictions from the least-squares regression line
Done www-awu.aleks.com AA O REGRESSION AND CORRELATION Predictions from the least-squares regression line = state afferent times. Typically, what the federal government finds is that "poor" states tend to stay poor and "wealthy" states tend to stay wealthy. Would we have been able to predict the 1999 per capita income for a state (denoted by y) from its 1980 per capita income (denoted by x)? The following bivariate data give the per capita income (in thousands of dollars) for a sample of states in the years 1980 and 1999 (source: U.S. Bureau of Economic Analysis, Survey of Current Business, May 2000). The data are plotted in the scatter plot below, and the least-squares regression line is drawn. The equation for this line is y=8.85 +1.87x. 1980 per 1999 per capita capita income, x income, y (in $1000s) (in $1000s) 9.1 22.3 9.4 26.2 9.6 25.3 10.0 26.5 9.3 27.4 8.5 27.2 8.1 25.1 8.4 25.0 9.7 29.7 10.8 30.7 9.4 26.1 10.2 8.8 11.8 10.9 10.2 Montana Missouri Arizona Texas Nebraska Georgia South Dakota Maine Rhode Island Delaware Indiana Wisconsin Louisiana Nevada Washington Oregon Send data to calculator V 27.4 22.8 30.4 30.3 27.1 Send data to Excel a income, y (in $1000s) greater than less than 1999 per 38- 364 344 324 30. 28. 26 24 O▬▬▬▬ 0/5 22, 1980 per capita income, x (in $1000s) Based on the sample data and the regression line, complete the following. (a) For these data, 1980 per capita incomes that are greater than the mean of the 1980 per capita incomes tend to be paired with 1999 per capita incomes that are (Choose one) the mean of the 1999 per capita incomes. X 5
Transcribed Image Text:Done www-awu.aleks.com AA O REGRESSION AND CORRELATION Predictions from the least-squares regression line = state afferent times. Typically, what the federal government finds is that "poor" states tend to stay poor and "wealthy" states tend to stay wealthy. Would we have been able to predict the 1999 per capita income for a state (denoted by y) from its 1980 per capita income (denoted by x)? The following bivariate data give the per capita income (in thousands of dollars) for a sample of states in the years 1980 and 1999 (source: U.S. Bureau of Economic Analysis, Survey of Current Business, May 2000). The data are plotted in the scatter plot below, and the least-squares regression line is drawn. The equation for this line is y=8.85 +1.87x. 1980 per 1999 per capita capita income, x income, y (in $1000s) (in $1000s) 9.1 22.3 9.4 26.2 9.6 25.3 10.0 26.5 9.3 27.4 8.5 27.2 8.1 25.1 8.4 25.0 9.7 29.7 10.8 30.7 9.4 26.1 10.2 8.8 11.8 10.9 10.2 Montana Missouri Arizona Texas Nebraska Georgia South Dakota Maine Rhode Island Delaware Indiana Wisconsin Louisiana Nevada Washington Oregon Send data to calculator V 27.4 22.8 30.4 30.3 27.1 Send data to Excel a income, y (in $1000s) greater than less than 1999 per 38- 364 344 324 30. 28. 26 24 O▬▬▬▬ 0/5 22, 1980 per capita income, x (in $1000s) Based on the sample data and the regression line, complete the following. (a) For these data, 1980 per capita incomes that are greater than the mean of the 1980 per capita incomes tend to be paired with 1999 per capita incomes that are (Choose one) the mean of the 1999 per capita incomes. X 5
In ongoing economic analyses, the U.S. federal government compares per capita incomes not only among different states but also for the same state at different times. Typically, what the federal government finds is that "poor" states tend to stay poor and "wealthy" states tend to stay wealthy. Would we have been able to predict the 1999 per capita income for a state (denoted by y) from its 1980 per capita income (denoted by x)? The following bivariate data give the per capita income (in thousands of dollars) for a sample of states in the years 1980 and 1999 (source: U.S. Bureau of Economic Analysis, Survey of Current Business, May 2000). The data are plotted in the scatter plot below, and the least-squares regression line is drawn. The equation for this line is -8.85+1.87x. Montana Missouri Arizona Texas Texas Nebraska Georgia Georgia South Dakota Maine Rhode Island Delaware Indiana Wisconsin Louisiana Nevada 1980 per capita income, x (in $1000s) 9.1 9.4 9.6 10.0 9.3 3:5 Washington Oregon Send data to calculator 8.5 8.5 8.1 8.1 8.4 9.7 10.8 9.4 10.2 8.8 11.8 10.9 10.2 1999 per capita income, y (in $1000s) 22.3 26.2 25.3 26.5 20.3 27.4 414 27.2 21.2 25.1 25.0 29 7 29.7 30.7 26.1 27.4 22.8 30.4 30.3 27.1 Send data to Excel 1999 per capita in (in $1000s) 1980 per capita income, x (in $1000s) Based on the sample data and the regression line, complete the following. (a) For these data, 1980 per capita incomes that are greater than the mean of the 1980 per capita incomes tend to be paired with 1999 per capita incomes that are (Choose one) the mean of the 1999 per capita incomes. (b) According to the regression equation, for an increase of one thousand dollars in 1980 per capita income, there is a corresponding increase of how many thousand dollars in 1999 per capita income? (Do not round your answer.) 0 (c) From the regression equation, what is the predicted 1999 per capita income (in thousands of dollars) when the 1980 per capita income is 9.3 thousand dollars? (Round your answer to at least one decimal place.) 0 (d) From the regression equation, what is the predicted 1999 per capita income (in thousands of dollars) when the 1980 per capita income is 10.3 thousand dollars? (Round your answer to at least one decimal place.) 0 X $
Transcribed Image Text:In ongoing economic analyses, the U.S. federal government compares per capita incomes not only among different states but also for the same state at different times. Typically, what the federal government finds is that "poor" states tend to stay poor and "wealthy" states tend to stay wealthy. Would we have been able to predict the 1999 per capita income for a state (denoted by y) from its 1980 per capita income (denoted by x)? The following bivariate data give the per capita income (in thousands of dollars) for a sample of states in the years 1980 and 1999 (source: U.S. Bureau of Economic Analysis, Survey of Current Business, May 2000). The data are plotted in the scatter plot below, and the least-squares regression line is drawn. The equation for this line is -8.85+1.87x. Montana Missouri Arizona Texas Texas Nebraska Georgia Georgia South Dakota Maine Rhode Island Delaware Indiana Wisconsin Louisiana Nevada 1980 per capita income, x (in $1000s) 9.1 9.4 9.6 10.0 9.3 3:5 Washington Oregon Send data to calculator 8.5 8.5 8.1 8.1 8.4 9.7 10.8 9.4 10.2 8.8 11.8 10.9 10.2 1999 per capita income, y (in $1000s) 22.3 26.2 25.3 26.5 20.3 27.4 414 27.2 21.2 25.1 25.0 29 7 29.7 30.7 26.1 27.4 22.8 30.4 30.3 27.1 Send data to Excel 1999 per capita in (in $1000s) 1980 per capita income, x (in $1000s) Based on the sample data and the regression line, complete the following. (a) For these data, 1980 per capita incomes that are greater than the mean of the 1980 per capita incomes tend to be paired with 1999 per capita incomes that are (Choose one) the mean of the 1999 per capita incomes. (b) According to the regression equation, for an increase of one thousand dollars in 1980 per capita income, there is a corresponding increase of how many thousand dollars in 1999 per capita income? (Do not round your answer.) 0 (c) From the regression equation, what is the predicted 1999 per capita income (in thousands of dollars) when the 1980 per capita income is 9.3 thousand dollars? (Round your answer to at least one decimal place.) 0 (d) From the regression equation, what is the predicted 1999 per capita income (in thousands of dollars) when the 1980 per capita income is 10.3 thousand dollars? (Round your answer to at least one decimal place.) 0 X $
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