Question

In: Finance

Based on the below data what will be the value of standard error? Regression Statistics Multiple...

Based on the below data what will be the value of standard error?

Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations 8
ANOVA
df SS MS F
Regression 1 33 33.0 16.5
Residual 6 12 2.0
Total 7
Coefficients Standard Error t Stat P-value
Intercept 9 31.274666 3.984284 0.007248
Advertising (thousands of $) 24 6.19330674 1.610802 0.158349

Submit

Answer format: Number: Round to: 2 decimal places.

Solutions

Expert Solution

standard error = SQRT(sums of square of residuals/ no of degrees of freedom)

standard error = SQRT(SS of residual /df) = SQRT (12/6) = SQRT (2) = 1.4142 = 1.41

Answer : 1.41 [Thumbs up please]

jeff jeffy answered 2 years ago
Next > < Previous

Related Solutions

Based on the below data what will be the value of multiple R?
Based on the below data what will be the value of multiple R?Regression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservations8ANOVAdfSSMSFRegression132328Residual6284Total7CoefficientsStandard Errort StatP-valueIntercept1131.2746663.9842840.007248Advertising (thousands of $)196.193306741.6108020.158349
What is Standard Error in a Regression?
  What is Standard Error in a Regression? The average deviation from the mean The average deviation of the correlation coefficient to the R2 value The average deviation of X to Y values The average deviation of the predicted scores and the actual scores
The regression line, standard error, and Sxx for the data set are given below. The predictor...
The regression line, standard error, and Sxx for the data set are given below. The predictor value is x=2. Use the information to do parts (a) through (d). Y(hat)=2.00+0.00x Se=2.64575 Sxx= 5 x values: 3,4,1,2 y values: 2,3,4,-1 Part A) Determine a point estimate for the conditional mean of the response variable corresponding to the specified value of the predictor variable. Part B) Find a 90% confidence interval for the response variable corresponding to the specified value of the predictor...
Using Excel: Regression Statistics Multiple R 0.9021 R- Square 0.8138 Adjusted R Square 0.7828 Standard Error...
Using Excel: Regression Statistics Multiple R 0.9021 R- Square 0.8138 Adjusted R Square 0.7828 Standard Error 9.4006 ANOVA df SS MS F Regression 1 2317.6 2317.6 26.226 Residual 6 530.23 88.372 Total 7 2847.9 Coefficients Standard Error t Stat P-value Intercept 45.897 5.5447 8.2776 0.0002 Number of Surgeries (x) 5.1951 1.0144 5.1211 0.0022 1. r = 0.90 strong positive correlation 2. y = 5.195 x + 45.897 , 3. r2 = 0.8138 , and 4. Se =  9.4006 5. Results of...
SUMMARY OUTPUT Regression Statistics Multiple R 0.727076179 R Square 0.528639771 Adjusted R Square 0.525504337 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.727076179 R Square 0.528639771 Adjusted R Square 0.525504337 Standard Error 3.573206748 Observations 455 ANOVA df SS MS F Significance F Regression 3 6458.025113 2152.67504 168.601791 2.7119E-73 Residual 451 5758.280717 12.7678065 Total 454 12216.30583 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept -0.250148858 0.359211364 -0.6963835 0.48654745 -0.9560846 0.45578693 -1.1793476 0.67904987 RBUK 0.025079378 0.023812698 1.05319345 0.29281626 -0.0217182 0.07187699 -0.0365187 0.08667745 RSUS 0.713727515 0.042328316 16.8617037 8.0578E-50 0.6305423 0.79691273 0.60423372 0.82322131...
SUMMARY OUTPUT Regression Statistics Multiple R 0.72707618 R Square 0.52863977 Adjusted R Square 0.52550434 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.72707618 R Square 0.52863977 Adjusted R Square 0.52550434 Standard Error 3.57320675 Observations 455 ANOVA df SS MS F Significance F Regression 3 6458.02511 2152.67504 168.601791 2.7119E-73 Residual 451 5758.28072 12.7678065 Total 454 12216.3058 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept -0.2501489 0.35921136 -0.6963835 0.48654745 -0.9560846 0.45578693 -1.1793476 0.67904987 RUK 0.02507938 0.0238127 1.05319345 0.29281626 -0.0217182 0.07187699 -0.0365187 0.08667745 RSUS 0.71372752 0.04232832 16.8617037 8.0578E-50 0.6305423 0.79691273 0.60423372 0.82322131...
SUMMARY OUTPUT Regression Statistics Multiple R 0.195389 R Square 0.038177 Adjusted R Square 0.037333 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.195389 R Square 0.038177 Adjusted R Square 0.037333 Standard Error 13.69067 Observations 1142 ANOVA df SS MS F Significance F Regression 1 8481.255 8481.255 45.2492 2.74E-11 Residual 1140 213675.2 187.4344 Total 1141 222156.4 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 40.19631 0.596741 67.35967 0 39.02547 41.36714 39.02547 41.36714 X Variable 1 7.31E-05 1.09E-05 6.726752 2.74E-11 5.18E-05 9.45E-05 5.18E-05 9.45E-05 Discuss the statistical significance of the model...
SUMMARY OUTPUT Regression Statistics Multiple R 0.396235 R Square 0.157002 Adjusted R Square 0.156262 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.396235 R Square 0.157002 Adjusted R Square 0.156262 Standard Error 18.42647 Observations 1142 ANOVA df SS MS F Significance F Regression 1 72088.71 72088.71 212.3161 3.12E-44 Residual 1140 387069.6 339.5348 Total 1141 459158.4 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 26.35917 0.803163 32.8192 7.4E-167 24.78333 27.93501 24.78333 27.93501 X Variable 1 0.000213 1.46E-05 14.57107 3.12E-44 0.000184 0.000242 0.000184 0.000242 a. Write the reqression equation. Discuss the...
SUMMARY OUTPUT Regression Statistics Multiple R 0.195389 R Square 0.038177 Adjusted R Square 0.037333 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.195389 R Square 0.038177 Adjusted R Square 0.037333 Standard Error 36578.71 Observations 1142 ANOVA df SS MS F Significance F Regression 1 6.05E+10 6.05E+10 45.2492 2.74E-11 Residual 1140 1.53E+12 1.34E+09 Total 1141 1.59E+12 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 17779.38 3518.846 5.052617 5.07E-07 10875.24 24683.53 10875.24 24683.53 X Variable 1 522.0407 77.60665 6.726752 2.74E-11 369.7728 674.3086 369.7728 674.3086 Income using age Write the regression equation....
Regression Statistics Multiple R 0.451216205 R Square 0.203596063 Adjusted R Square 0.190097692 Standard Error 0.051791629 Observations...
Regression Statistics Multiple R 0.451216205 R Square 0.203596063 Adjusted R Square 0.190097692 Standard Error 0.051791629 Observations 61 ANOVA df SS MS F Significance F Regression 1 0.040458253 0.040458253 15.083009 0.000262577 Residual 59 0.158259997 0.002682373 Total 60 0.19871825 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 0.00987396 0.006785133 1.455234544 0.150904641 -0.00370306 0.023450979 -0.00370306 0.023450979 S&P 0.752212332 0.193685208 3.883684976 0.000262577 0.364649126 1.139775537 0.364649126 1.139775537 Current estimate given to us in the directions 1.07 RESIDUAL OUTPUT...
ADVERTISEMENT
Subjects
ADVERTISEMENT
Latest Questions
ADVERTISEMENT