Data: (yix ); i= 1, 30 ii) Ho:B1 = 2 vs. H1:1#2 Model - Test Statistic: Call: -

Question

Data: (yix ); i= 1, 30 ii) Ho:B1 = 2 vs. H1:1#2 Model - Test Statistic: Call: - State the t value you would compare your test statistic to for this hypothesis test: lm(formula = y ~ x1 + x2) Conclusion: Residuals: Min 1Q Median Max -1.6232 -0.6626 0.0234 0.4180 1.7137 Coefficients: Estimate Std. Error t value Pr(>t|) (Intercept) 5.5934 2.3223 2.409 0.0231 0.3568 5.566 6.69e-06 x1 1.9859 x2 5.0974 0.4146 12.296 1.42e-12 * Signif. codes: 0 0.001 0.010.050.11 Residual standard error: 0.8867 on 27 degrees of freedorm Multiple R-squared: 0.875 F-statistic: 94.64 on 2 and 27 DF, p-value: 6.322e-13 2, Adjusted R-squared: 0.8659 Analysis of Variance Table Response: y Df Sum Sq Mean Sq F value Pr(>F) 1 29.951 29.951 38.091 1.347e-06* 1 118.874 118.874 151.183 1.419e-12 Residuals 27 21.230 0.786

Explanation / Answer

TS = (b1^ - b1 under null)/se(b1^)
= (1.9859 - 2)/0.3568
= -0.03951

df = n-k-1 = 30-2-1= 27
t-value = 2.0518

since |TS| < critical value
we fail to reject the null hypothesis

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