In order to explain the U.S. defense budget, the following variables are considered. Data from1962-1981 is collected.VariableDefinitionYDefense budget-outlay for year t, $ billionsX2GNP for year t, $ billionsX3U.S. military sales/assistance in year t, $ billionsX4Aerospace industry sales, $ billionsDependent Variable: YMethod: Least SquaresDate: 11/22/21 Time: 11:20Sample: 1962 1981Included observations: 20VariableCoefficientStd. Errort-StatisticProb.X20.0167030.0070172.3802610.0301X3-0.6961740.453978-1.5334970.1447X41.4677290.2776085.2870470.000122.775143.3116956.8771860.0000R-squared0.971088Mean dependent var83.86000Adjusted R-squared0.965667S.D. dependent var28.97771S.E. of regressionSum squared resid5.369339Akaike info criterion6.376143461.2769Schwarz criterion6.575290Log likelihood-59.76143Hannan-Quinn criter.6.415019F-statistic179.1337Durbin-Watson stat0.676777Prob(F-statistic)0.000000 d) Identify which variables are negatively related with the defence budget outlay. Howcan you identify?e) Specify and interpret the value of R2 for the regression.f) Test at 5% significance level whether the model has any explanatory power by usingR².

Regression is used when we want to predict continuous dependent variable from a number of…

Answered: d) Identify which variables are…

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter7: Distance And Approximation

Section7.3: Least Squares Approximation

Problem 31EQ

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d) Identify which variables are negatively related with the defence budget outlay. How can you identify? e) Specify and interpret the value of R² for the regression. f) Test at 5% significance level whether the model has any explanatory power by using R².