Using the data on consumption expenditure Y, Income X2, and wealth X3, following output isgenerated:Dependent Variable: YMethod: Least SquaresDate: 11/22/21 Time: 11:44Sample: 1 10Included observations: 10VariableCoefficientStd. Errort-StatisticProb.X20.9415370.8228981.1441720.2902X3-0.0424350.080664-0.5260620.615124.774736.7525003.6689720.0080Mean dependent varS.D. dependent varR-squared0.963504111.0000Adjusted R-squared0.95307731.42893S.E. of regression6.808041Akaike info criterion6.917411Sum squared residLog likelihood324.4459Schwarz criterion7.008186-31.58705Hannan-Quinn criter.6.817830F-statistic92.40196Durbin-Watson stat2.890614Prob(F-statistic)0.000009a) State the estimated OLS regression. Identify the slope coefficients that may not besignificant and provide justifications for your answer.b) Specify and interpret the value of R² for the regression.

The result of a OLS model is given for the dependent variable expenditure Y and the independent…

Answered: Using the data on consumption…

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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On minitab or excel Calculating the coefficient of determination of a set of data, it was obtained that r^2 = 0.845 . Based on this value, it can be concluded that: Options: The least squares line provided a good fit as a large proportion of the variability in "y" has been explained by the least squares line. The least squares line provided a good fit as a small proportion of the variability in "y" has been explained by the least squares line. The least squares line did not provide a good fit since a small proportion of the variability in "y" has been explained by the least squares line. The least squares line did not provide a good fit since a large proportion of the variability in "y" has been explained by the least squares line.
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The output table below represents the results of the estimation of household expenditures (Y) and income (X) in thousand dollars. Considering the output table below, answer the following questions. Dependent Variable: Y Method: Least Squares Date: 12/28/16 Time: 15:29 Sample: 2000 2010 Included observations: 11 Variable Coefficient Std. Error t-Statistic Prob. C 0.229334 2.938536 0.078044 0.9395 X 0.354833 0.024783 14.31783 0.0000 R-squared 0.957944 Mean dependent var 37.45455 Adjusted R-squared 0.953271 S.D. dependent var 21.01125 S.E. of regression 4.541976 Akaike info criterion 6.027567 Sum squared resid 185.6659 Schwarz criterion 6.099911 Log likelihood -31.15162 Hannan-Quinn criter. 5.981964…
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The output table below represents the results of the estimation of household expenditures (Y) and income (X) in thousand dollars. Considering the output table below, answer the following questions. Dependent Variable: Y Method: Least Squares Date: 12/28/16 Time: 15:29 Sample: 2000 2010 Included observations: 11 Variable Coefficient Std. Error t-Statistic Prob. C 0.229334 2.938536 0.078044 0.9395 X 0.354833 0.024783 14.31783 0.0000 R-squared 0.957944 Mean dependent var 37.45455 Adjusted R-squared 0.953271 S.D. dependent var 21.01125 S.E. of regression 4.541976 Akaike info criterion 6.027567 Sum squared resid 185.6659 Schwarz criterion 6.099911 Log likelihood -31.15162 Hannan-Quinn criter. 5.981964…
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