4- Indicate if the estimates are statistically significant at 0.1%, 1%, 5% or 10%. Regression summary...
4- Indicate if the estimates are statistically significant at 0.1%, 1%, 5% or 10%.
Regression summary output using Excel is as follows.
|
SUMMARY OUTPUT |
||||
|
Regression Statistics |
||||
|
Multiple R |
0.8811 |
|||
|
R Square |
0.7764 |
|||
|
Adjusted R Square |
0.7205 |
|||
|
Standard Error |
14.7724 |
|||
|
Observations |
16 |
|||
|
ANOVA |
||||
|
df |
SS |
MS |
F |
|
|
Regression |
3 |
9091.7392 |
3030.5797 |
13.8874 |
|
Residual |
12 |
2618.7008 |
218.2251 |
|
|
Total |
15 |
11710.44 |
||
|
Coefficients |
Standard Error |
t Stat |
P-value |
|
|
Intercept |
29.1385 |
174.7427 |
0.1668 |
0.8703 |
|
PFH |
-2.1236 |
0.3405 |
-6.2361 |
0.0000 |
|
PR |
1.0345 |
0.4667 |
2.2164 |
0.0467 |
|
M |
3.0871 |
0.9993 |
3.0892 |
0.0094 |
Homework Answers
Add Answer to:
4- Indicate if the estimates are statistically significant at
0.1%, 1%, 5% or 10%.
Regression summary...
5- Interpret the coefficient of determination (R-squared) and the F test. SUMMARY OUTPUT Regression Statistics Multiple...
5- Interpret the coefficient of determination (R-squared) and the F test. SUMMARY OUTPUT Regression Statistics Multiple R 0.8811 R Square 0.7764 Adjusted R Square 0.7205 Standard Error 14.7724 Observations 16 ANOVA df SS MS F Regression 3 9091.7392 3030.5797 13.8874 Residual 12 2618.7008 218.2251 Total 15 11710.44 Coefficients Standard Error t Stat P-value Intercept 29.1385 174.7427 0.1668 0.8703 PFH -2.1236 0.3405 -6.2361 0.0000 PR 1.0345 0.4667 2.2164 0.0467 M 3.0871 0.9993 3.0892 0.0094
Following a regression analysis output : SUMMARY OUTPUT Regression Statistics Multiple R 0.719422 R Square Adjusted...
Following a regression analysis output : SUMMARY OUTPUT Regression Statistics Multiple R 0.719422 R Square Adjusted R Square 0.477366 Standard Error Observations 14 ANOVA df SS MS F Regression 1 3.028885709 Residual 12 2.823257148 Total 13 5.852142857 Coefficients Standard Error t Stat P-value Intercept 1.157091 0.566482479 0.063699302 Satisfaction with Speed of Execution 0.636798 0.177478218 0.003726861 Group of answer choices R Square is 0.517 Standard error is 0.386 Residuals are 2.823 F-test is 11.87 R Square is 0.517 Standard error is...
In the following table, which parameter is the most significant; least significant; why? SUMMARY OUTPUT Regression...
In the following table, which parameter is the most significant;
least significant; why?
SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9613 0.9241 0.9222 1425.3397 200 ANOVA Significance df MS Regression Residual Total 479410417802.47 95882083560.49 472.4892 39368362197.53 518778780000.00 0.00 194 199 202929702.05 Upper 95% value 2.34 0.0201 CoefficientsStandard Erro t Stat Lower 95% 45482.366 -10383.543 11.088 738.388 0.014 2.546 19403.8863 3153.7202 10.4859 175.8223 0.0023 1.2209 Intercept V1 v2 7217.90 83746.83 -3.290.001216602.68 -4164.41 31.77 391.67...
12. (9 Points) Using the Regression Computer Output to the right, find/calculate: SUMMARY OUTPUT Regression Statistics...
12. (9 Points) Using the Regression Computer Output to the right, find/calculate: SUMMARY OUTPUT Regression Statistics Multiple R 0.928717 R Square 0.862516 Adjusted R Square 0.835019 Standard Error 1.987885 Observations (A) The value of the Slope, b. ANOVA df 31.36785 a. 1.103933 b. 18.16292 c. 5.6007 d. 3.951685 e. 2.004566 Regression Residual Total SS MS 123.9559 123.9559 19.758433.951685 143.7143 Intercept Coefficients 18.16292 1.103933 Standard Error 2.004566 0.197106 t Stat 9.060773 5.6007 P-value 0.000274 0.002507 (B) The value of the Correlation...
Dep.= % WRK Indep.= % MGT SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R...
Dep.= % WRK Indep.= % MGT SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Significance df SS MS F F Regression 102.1488 148.9539 Residual Total 12.0000 Standard Coefficients Error t Stat P-value Lower 95% Upper 95% Intercept % MGT 0.4543 SE CI CI PI PI Predicted Predicted Lower Upper Lower Upper x0 Value Value 95% 95% 95% 95% 67.0000 67.8474 65.8779 69.8169 72.0000 70.1189 68.2003 72.0375 76.0000 71.9361 69.7884 74.0838 Dep.= % MGT...
In the following table, which parameters are significant and why? SUMMARY OUTPUT Regression Statistics Multiple R...
In the following table, which parameters are significant and
why?
SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9613 0.9241 0.9222 1425.3397 200 ANOVA Significance df MS Regression Residual Total 479410417802.47 95882083560.49 472.4892 39368362197.53 518778780000.00 0.00 194 199 202929702.05 Upper 95% value 2.34 0.0201 CoefficientsStandard Erro t Stat Lower 95% 45482.366 -10383.543 11.088 738.388 0.014 2.546 19403.8863 3153.7202 10.4859 175.8223 0.0023 1.2209 Intercept V1 v2 7217.90 83746.83 -3.290.001216602.68 -4164.41 31.77 391.67 1085.11 0.02 0.14...
What is the SCL equation for your regression output? A D 1 SUMMARY OUTPUT 2 Regression Statistics L3 4 Multiple R 5...
What is the SCL equation for your regression output?
A D 1 SUMMARY OUTPUT 2 Regression Statistics L3 4 Multiple R 5 R Square 6 Adjusted R Square 7 Standard Error 8 Observations 0.147788325 0.021841389 0.004680712 0.112847351 59 10 11 12 Coefficients Standard Error P-value 0.015008141 0.043055201 0.965808003 t Stat 13 Intercept 14 S&P 500 HPR 0.000646179 0.490477908 0.434756954 1.128165755 0.263976201 15
A D 1 SUMMARY OUTPUT 2 Regression Statistics L3 4 Multiple R 5 R Square 6 Adjusted R...
1st regression analysis 2nd regression analysis 1. Analyze the two regression analysis's above ...
1st regression analysis
2nd regression analysis
1. Analyze the two regression analysis's above and make
a recommendation on if the organization should increase, decrease,
or retain their pricing and why?
2. What happens to the dependent variable Y if the price
X1 decreases in the second regression analysis?
SUMMARY OUTPUT Y=UNITS SOLD X=PRICE Regression Statistics Multiple R R Square Adiusted R S Standard Error Observations 0.874493978 0.764739718 0.756026374 159.2178137 29 quare ANOVA df MS Significance F 1 2224908.261 2224908.26187.76650338 5.64792E-10...
SUMMARY OUTPUT Regression Statistics Multiple R 0.818616296 R Squa...
SUMMARY OUTPUT Regression Statistics Multiple R 0.818616296 R Square 0.67013264 Adjusted R Square 0.658351663 Standard Error 9.16867179 Observations 30 ANOVA df SS MS F Significance F Regression 1 4781.80995 4781.80995 56.8826 3.2455E-08 Residual 28 2353.807187 84.06454239 Total 29 7135.617137 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 28.21496731 3.739591617 7.544932763 3.22E-08 20.55476114 35.87517349 Dividend 2.367177613 0.313863719 7.542055589 3.25E-08 1.724256931 3.010098296 c. You run a regression analysis using Data Analysis to answer the following question: Is stock selling...
7,10,11 Based on the following regression output, what is the equation of the regression line? Regression...
7,10,11
Based on the following regression output, what is the equation of the regression line? Regression Statistics Multiple R 0.917214 R Square 0.841282 Adjusted R Square 0.821442 Standard Error 9.385572 Observations 10 ANOVA df SS MS Significance F 1 Regression 3735.3060 3735.30600 42.40379 0.000186 8 Residual 704.7117 88.08896 9 Total 4440.0170 Coefficients Standard Error t Stat P-value Lower 95% Intercept 31.623780 10.442970 3.028236 0.016353 7.542233 X Variable 1.131661 0.173786 6.511819 0.000186 0.730910 o a. 9; = 7.542233+0.7309 Xli o b....
In the following table, which parameter is the most significant;
least significant; why?
SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9613 0.9241 0.9222 1425.3397 200 ANOVA Significance df MS Regression Residual Total 479410417802.47 95882083560.49 472.4892 39368362197.53 518778780000.00 0.00 194 199 202929702.05 Upper 95% value 2.34 0.0201 CoefficientsStandard Erro t Stat Lower 95% 45482.366 -10383.543 11.088 738.388 0.014 2.546 19403.8863 3153.7202 10.4859 175.8223 0.0023 1.2209 Intercept V1 v2 7217.90 83746.83 -3.290.001216602.68 -4164.41 31.77 391.67...
12. (9 Points) Using the Regression Computer Output to the right, find/calculate: SUMMARY OUTPUT Regression Statistics Multiple R 0.928717 R Square 0.862516 Adjusted R Square 0.835019 Standard Error 1.987885 Observations (A) The value of the Slope, b. ANOVA df 31.36785 a. 1.103933 b. 18.16292 c. 5.6007 d. 3.951685 e. 2.004566 Regression Residual Total SS MS 123.9559 123.9559 19.758433.951685 143.7143 Intercept Coefficients 18.16292 1.103933 Standard Error 2.004566 0.197106 t Stat 9.060773 5.6007 P-value 0.000274 0.002507 (B) The value of the Correlation...
In the following table, which parameters are significant and
why?
SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9613 0.9241 0.9222 1425.3397 200 ANOVA Significance df MS Regression Residual Total 479410417802.47 95882083560.49 472.4892 39368362197.53 518778780000.00 0.00 194 199 202929702.05 Upper 95% value 2.34 0.0201 CoefficientsStandard Erro t Stat Lower 95% 45482.366 -10383.543 11.088 738.388 0.014 2.546 19403.8863 3153.7202 10.4859 175.8223 0.0023 1.2209 Intercept V1 v2 7217.90 83746.83 -3.290.001216602.68 -4164.41 31.77 391.67 1085.11 0.02 0.14...
What is the SCL equation for your regression output?
A D 1 SUMMARY OUTPUT 2 Regression Statistics L3 4 Multiple R 5 R Square 6 Adjusted R Square 7 Standard Error 8 Observations 0.147788325 0.021841389 0.004680712 0.112847351 59 10 11 12 Coefficients Standard Error P-value 0.015008141 0.043055201 0.965808003 t Stat 13 Intercept 14 S&P 500 HPR 0.000646179 0.490477908 0.434756954 1.128165755 0.263976201 15
A D 1 SUMMARY OUTPUT 2 Regression Statistics L3 4 Multiple R 5 R Square 6 Adjusted R...
1st regression analysis
2nd regression analysis
1. Analyze the two regression analysis's above and make
a recommendation on if the organization should increase, decrease,
or retain their pricing and why?
2. What happens to the dependent variable Y if the price
X1 decreases in the second regression analysis?
SUMMARY OUTPUT Y=UNITS SOLD X=PRICE Regression Statistics Multiple R R Square Adiusted R S Standard Error Observations 0.874493978 0.764739718 0.756026374 159.2178137 29 quare ANOVA df MS Significance F 1 2224908.261 2224908.26187.76650338 5.64792E-10...
7,10,11
Based on the following regression output, what is the equation of the regression line? Regression Statistics Multiple R 0.917214 R Square 0.841282 Adjusted R Square 0.821442 Standard Error 9.385572 Observations 10 ANOVA df SS MS Significance F 1 Regression 3735.3060 3735.30600 42.40379 0.000186 8 Residual 704.7117 88.08896 9 Total 4440.0170 Coefficients Standard Error t Stat P-value Lower 95% Intercept 31.623780 10.442970 3.028236 0.016353 7.542233 X Variable 1.131661 0.173786 6.511819 0.000186 0.730910 o a. 9; = 7.542233+0.7309 Xli o b....
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