Exhibit 15-6 Below you are given a partial Excel output based on a sample of 16 observations. ANOVA df SS MS F Regression 4 853 2 426.5 Residual 485.3 Coefficients Standard Error Intercept 12.924 4.425 x1 -3.682 2.630 x2 45.216 12.560 We want to test whether the parameter β1 is significant. The test statistic equals _____. a. 3.6 b. 1.4 c. -1.4 d. 5
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From the output, we can see that β1 = -3.682 and SE(β1) = 2.630. Plugging these values into the formula, we get: t = (-3.682 - 0) / 2.630 = -1.400 Show more…
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Exhibit 15-6: Below you are given partial Excel output based on a sample of 16 observations. ANOVA MS Regression Residual 4,853 2,426.5 485.3 Coefficients 12.924 Standard Error 4.425 2.630 12.560 Intercept -3.682 45.216 The t value obtained from the table that is used to test an individual parameter at the 1% level is: a. 2.977 b. 2.921 c. 3.012 d. 2.65
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Exhibit 15-6: Below you are given a partial Excel output based on a sample of 16 observations. ANOVA df SS MS F Regression 4,853 2,426.5 Residual 485.3 Coefficients Standard Error Intercept 12.924 4.425 x1 -3.682 2.630 x2 45.216 12.560 The estimated regression equation is a. ŷ = 12.924 - 3.682 x1 + 45.216 x2 b. ŷ = 4.425 + 2.63 x1 + 12.56 x2 c. y = β0 + β1x1 + β2x2 + ε d. E(y) = β0 + β1x1 + β2x2
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Below you are given a partial Excel output based on a sample of 16 observations. ANOVA df SS MS F Regression 4,853 2,426.5 Residual 485.3 Coefficients Standard Error Intercept 12.924 4.425 x1 -3.682 2.630 x2 45.216 12.560 Carry out the test to determine if there is a relationship among the variables at the 5% level. The null hypothesis should ______.
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