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 |
Carry out the test to determine if there is a relationship among
the variables at the 5% level. The null hypothesis should
______.
|
|||
|
|||
|
|||
|
from abvoe test statistic F =2426.5/485.3 =5
for 2,13 degree of freedom, crtiical value at 5 % level =finv(0.05,2,13)=3.806
since test statistic > critical value ; option C is correct
c. be rejected
Exhibit 15-6 Below you are given a partial Excel output based on a sample of 16...
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 585.3 Coefficients Standard Error Intercept 12.924 4.425 x1 -3.682 1.630 x2 45.216 22.560 A) The degrees of freedom for the sum of squares explained by the regression (SSR) are _____. B) The sum of squares due to error (SSE) equals _____. C) The test statistic used to determine if there is a relationship among...
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 585.3 Coefficients Standard Error Intercept 12.924 4.425 x1 -3.682 1.630 x2 45.216 22.560 A) The interpretation of the coefficient of x 1 is that _____. B) We want to test whether the parameter β 1 is significant. The test statistic equals _____. C) The critical t value that is used to test an individual parameter...
Exhibit 15-6 Below you are given a partial computer output based on a sample of 16 observations. Coefficient Standard Error Constant 12.924 4.425 X1 -3.682 2.630 X2 45.216 12.560 Analysis of Variance Source of Variation Degrees of Freedom Sum of Squares Mean Square F Regression 4,853 2,426.5 Error 485.3 Refer to Exhibit 15-6. The estimated regression equation is Refer to Exhibit 15-6. The interpretation of the coefficient of X1 is that Refer to Exhibit 15-6. We want to test whether...
Question 1 (-110 Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations. Coefficients Standard Error Constant 12.924 4.425 -3.682 2.630 x2 45.216 12.560 Analysis of Variance Source of Variation Degrees of Freedom Sum of Squares Mean Square Regression 4853 2426.5 Error 485.3 Carry out the test to determine if there is a relationship among the variables at the 5% level. The null hypothesis should be rejected not be rejected...
Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations. Coefficients Standard Error Constant 12.924 4.425 x1 -3.682 2.630 x2 45.216 12.560 Analysis of Variance Source of Variation Degrees of Freedom Sum of Squares Mean Square F Regression 4853 2426.5 Error 485.3 We want to test whether the parameter β1 is significant. The test statistic equal a. -1.4. b. -5.0. c. 1.4. d. 3.6.
1 pts Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations. Standard Error Coefficients 4.425 Constant 12.924 2.630 -3.682 X1 45.216 12.560 X2 The interpretation of the coefficient of x is that a one unit increase inx1 will lead to a 3.682 unit decrease in x2 when all other variables are held constant. The unit of measurement for y is required to interpret the coefficient a one unit change...
Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations. Coefficients 12.924 Standard Error 4,425 Constant -3.682 2.630 45.216 12.560 Analysis of Variance Source of Variation Degrees of Freedom Sum of Squares Mean Square Regression 4853 2426.5 Error 485.3 The interpretation of the coefficient of Xi is that The interpretation of the coefficient of Xi is that O a one unit increase in xy will lead to a 3.682 unit...
Below you are given a partial computer output based on a sample of fifteen (15) observations. ANOVA df SS Regression 1 50.58 Residual Total 14 106.00 Coefficients Standard Error t Stat p-value Intercept 16.156 1.42 0.0000 Variable x -0.903 0.26 0.0000 The coefficient of determination is. 0.5228 0.4772 0.6535 0.3465
Below you are given a partial computer output based on a sample of fifteen (15) observations. ANOVA df SS Regression 50.58 Residual Total 14 106.00 Coefficients Standard Error tstat p-value 0.0000 Intercept 16.156 1.42 0.26 -0.903 0.0000 Variable x The estimated regression equation (also known as regression line fit) is O Y = B0+ B1X1 + E, O EY) = B0+ B 1X1 + B 2X2 Ý = -0.903 + 16.156X1 Ý = 1.42 + 0.26X1 none of the above
Table 4 Regression Model Y = α X1 + β X2 Parameter Estimates Coefficient Standard Error Constant 12.924 4.425 X1 -3.682 2.630 X2 45.216 12.560 Analysis of Variance Source of Degrees Sum of Mean Variation of Freedom Squares Square F Regression XXX 4,853 2,426.5 XXX Error XXX 485.3 Find above partial statistical output...