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) (i.e. test the hypothesis that all slope coefficients in the regression are zero). Test the overall significance of the regression in Table 2 at the 5% significance level

) (i.e. test the hypothesis that all slope coefficients in the regression are zero). Test the overall significance of the regression in Table 2 at the 5% significance level

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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q3 is for context, need help answering only the d section.

 

Thank you in advance

d) (i.e. test the hypothesis that all slope coefficients in the regression are zero). Test the overall significance of the regression in Table 2 at the 5% significance level
Transcribed Image Text:d) (i.e. test the hypothesis that all slope coefficients in the regression are zero). Test the overall significance of the regression in Table 2 at the 5% significance level
Question 3 We have a time series dataset from the US which includes the following variables for the period 1960- 1999: Yt: Per capita chicken consumption (in pounds) in year t. PC: The price of chicken (in cents per pound) in year t. PB: The price of beef (in cents per pound) in year t. YD: Per capita disposable income (in hundreds of dollars) in year t. YD2t: YD+ squared, i.e. YD2, = YD² We estimate by OLS the following regression model: Table 2 ■ . Dependent variable: Y Number of observations: 40 Variable intercept PC PB YD YD2 R-squared F statistic (p-value) Coefficient 27.6 -0.61 0.09 0.20 -0.0001 0.99 8.74 (0.006) Standard Error 1.58 0.16 0.04 0.01 0.00005
Transcribed Image Text:Question 3 We have a time series dataset from the US which includes the following variables for the period 1960- 1999: Yt: Per capita chicken consumption (in pounds) in year t. PC: The price of chicken (in cents per pound) in year t. PB: The price of beef (in cents per pound) in year t. YD: Per capita disposable income (in hundreds of dollars) in year t. YD2t: YD+ squared, i.e. YD2, = YD² We estimate by OLS the following regression model: Table 2 ■ . Dependent variable: Y Number of observations: 40 Variable intercept PC PB YD YD2 R-squared F statistic (p-value) Coefficient 27.6 -0.61 0.09 0.20 -0.0001 0.99 8.74 (0.006) Standard Error 1.58 0.16 0.04 0.01 0.00005
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