Question

In: Math

Consider the following regression output with Sunday circulation of newspapers as dependent variable and Daily circulation...

Consider the following regression output with Sunday circulation of newspapers as dependent variable and Daily circulation as independent variable. Both Sunday and Daily circulation and measured in thousands of copies.

Dependent Variable: Sunday

Variable:

Intercept

Daily

Coefficient

24.763

1.351

Std. Error

46.99

0.09

t Stat

0.527

14.532

P-value

0.602

0.000

Given the output above choose whether the following statement is TRUE or FALSE.

Question 1
This regression is bad because we are only 39.8% confident that the intercept coefficient is not 0.

a: TRUE

b: FALSE

Question 2 ( it part 2 of the first queestion)
Consider again the regression output of Question 1. How much confidence do you have that Daily increase of two thousand copies will result in Sunday increase of at least 2340 copies?

a: less than 50% confidence
b: at least 95% confidence but less than 99.7% confidence
c: at least 99.7% confidence

d: I do not have enough information to determine the confidence.

Solutions

Expert Solution

Consider the following regression output with Sunday circulation of newspapers as dependent variable and Daily circulation as independent variable. Both Sunday and Daily circulation and measured in thousands of copies.

Dependent Variable: Sunday

Variable:

Intercept

Daily

Coefficient

24.763

1.351

Std. Error

46.99

0.09

t Stat

0.527

14.532

P-value

0.602

0.000

Given the output above choose whether the following statement is TRUE or FALSE.

Question 1
This regression is bad because we are only 39.8% confident that the intercept coefficient is not 0.

a: TRUE

Answer:b: FALSE

intercept coefficient tells that when there is no daily circulation, the Sunday circulation is 24763.

Question 2 ( it part 2 of the first question)
Consider again the regression output of Question 1. How much confidence do you have that Daily increase of two thousand copies will result in Sunday increase of at least 2340 copies?

a: less than 50% confidence

b: at least 95% confidence but less than 99.7% confidence

Answer: c: at least 99.7% confidence

d: I do not have enough information to determine the confidence.

The regression coefficient is significant at 0.000 which is < 0.001 level of significance.

Daily increase of two thousand copies will result in Sunday increase of 2000*1.351 =2702 copies.


Related Solutions

The following output was obtained from a regression analysis of the dependent variable Rating and an...
The following output was obtained from a regression analysis of the dependent variable Rating and an independent variable Price. (10 points) ANOVA df SS MS F Regression 1 372.707 372.707 42.927 Residual 15 130.234 8.682 Total 16 502.941 Coefficients Standard Error t Stat P-value Intercept 45.623 3.630 12.569 0.000 Price 0.107 0.016 6.552 0.000 Use the critical value approach to perform an F test for the significance of the linear relationship between Rating and Price at the 0.05 level of...
The following regression output was obtained from a study of architectural firms. The dependent variable is...
The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 7.617 2.998 2.541 0.010 x1 0.224 0.069 3.246 0.000 x2 ? 1.144 0.559 ? 2.047 0.028 x3 ? 0.071 0.120 ? 0.592 0.114 x4 0.675 0.354 1.907 0.001 x5 ? 0.057 0.025 ? 2.280 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 2,113.40 422.7...
The following regression output was obtained from a study of architectural firms. The dependent variable is...
The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 7.617 2.998 2.541 0.010 x1 0.224 0.069 3.246 0.000 x2 ? 1.144 0.559 ? 2.047 0.028 x3 ? 0.071 0.120 ? 0.592 0.114 x4 0.675 0.354 1.907 0.001 x5 ? 0.057 0.025 ? 2.280 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 2,113.40 422.7...
The following regression output was obtained from a study of architectural firms. The dependent variable is...
The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 7.131 3.085 2.312 0.010 x1 0.203 0.170 1.194 0.000 x2 − 1.176 0.553 − 2.127 0.028 x3 − 0.089 0.311 − 0.286 0.114 x4 0.682 0.381 1.790 0.001 x5 − 0.030 0.025 − 1.200 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 1,946.79 389.4...
The following regression output was obtained from a study of architectural firms. The dependent variable is...
The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE T P-vaule Constant 7.096 3.245 2.187 0.010 x1 0.222 0.117 1.897 0.000 x2 -1.024 0.562 -1.822 0.028 x3 -0.337 0.192 -1.755 0.114 x4 0.623 0.263 2.369 0.001 x5 -0.056 0.029 -2.000 0.112 Analysis of Variance Source DF SS MS F P-Value Regression 5 2009.28 401.9 7.33 0.000 Residual Error 50 2741.54 54.83...
The following regression output was obtained from a study of architectural firms. The dependent variable is...
The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 7.166 3.075 2.330 0.010 x1 0.228 0.302 0.755 0.000 x2 − 1.184 0.586 − 2.020 0.028 x3 − 0.193 0.110 − 1.755 0.114 x4 0.572 0.293 1.952 0.001 x5 − 0.056 0.022 − 2.545 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 1,847.24 369.4...
The following regression output was obtained from a study of architectural firms. The dependent variable is...
The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 7.166 3.075 2.330 0.010 x1 0.228 0.302 0.755 0.000 x2 − 1.184 0.586 − 2.020 0.028 x3 − 0.193 0.110 − 1.755 0.114 x4 0.572 0.293 1.952 0.001 x5 − 0.056 0.022 − 2.545 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 1,847.24 369.4...
The following output was obtained from a regression analysis of the dependent variable Sales volume and...
The following output was obtained from a regression analysis of the dependent variable Sales volume and an independent variable “location distance from the downtown branch.       ANOVA df SS MS F Regression 2 552.137 267.069 24.235 Residual 10 110.22 11.02 Total 12 662.357 Coefficients Standard Error t Stat P-value Intercept -38.623 3.630 12.569 0.000 Location distance 0.309 0.016 6.552 0.000 Calculate and interpret the correlation coefficient. What does it tell us? (1.5 marks) Calculate and interpret the coefficient of determination. (1.5...
The following regression output was obtained from a study of architectural firms. The dependent variable is...
The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 7.131 3.085 2.312 0.010 x1 0.203 0.170 1.194 0.000 x2 − 1.176 0.553 − 2.127 0.028 x3 − 0.089 0.311 − 0.286 0.114 x4 0.682 0.381 1.790 0.001 x5 − 0.030 0.025 − 1.200 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 1,946.79 389.4...
The following regression output was obtained from a study of architectural firms. The dependent variable is...
The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars.    Predictor Coef SE Coef T     Constant 10.237 3.447 2.97 X1 0.392 -0.209 -1.88 X2 -0.392 -0.099 3.96 X3 0.207 -0.110 -1.88 X4 0.794 0.201 3.95 X5 -0.335 -0.126 2.66      Analysis of Variance   Source DF SS MS F   Regression 5 3710.00 742.00 15.14   Residual Error 48 2647.38 57.55   Total 53 6357.38    X1...
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT