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| Summary Output | ||||||||||
| Regression Statistics | ||||||||||
|
Multiple R
|
0.978724022 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|
R Square
|
0.957900711 | |||||||||
|
Adjusted R Square
|
0.952287472 | |||||||||
|
Standard Error
|
67.67055418 | |||||||||
|
Observations
|
18 | |||||||||
| ANOVA | ||||||||||
|
df
|
SS
|
MS
|
F
|
Significance F
|
||||||
|
Regression
|
2
|
1562918.941 |
781459.5
|
170.6503
|
4.80907E-11
|
|||||
|
Residual
|
15
|
68689.55855 |
4579.304
|
|||||||
|
Total
|
17
|
1631608.5 | ||||||||
|
Coefficients
|
Standard Error
|
t Stat
|
P-value
|
|||||||
|
Intercept
|
1959.709718
|
306.4905312
|
6.39403
|
1.21E-05
|
||||||
|
X1
|
-0.469657287
|
0.264557168
|
-1.77526
|
0.096144
|
||||||
|
X2
|
-2.163344882
|
0.278361425
|
-7.77171
|
1.23E-06
|
The overall proportion of variation of y accounted by x1 and x2 is _______
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- The following Excel tables are obtained when "Score received on an exam (measured in percentage points)" is regressed on "percentage attendance" for 22 students in a Statistics for Business and Economics course. Regression Statistics Multiple R R Square Standard Error 20.25979924 0.142620229 0.02034053 Observations 22 Coefficients Standard Error T Stat Intercept 39.39027309 37.24347659 1.057642216 Attendance 0.340583573 0.52852452 0.644404489 Estimate the "Score received on an exam" if "percentage attendance" student is 75. Select one: a. 39.6 b. 45.6 c 78.7 d. 64.9A real estate builder wishes to determine how house size (House) is influenced by family income (Income) and family size (Size). House size is measured in meter square and income is measured in IDR millions. The builder randomly selected 50 families and ran the multiple regression. Partial Microsoft Excel output is provided below: Regression Statistics Multiple R 0.8479 R Square 0.7189 Adjusted R Square 0.7069 Standard Error 17.5571 Observations 50 ANOVA df SS MS F Significance F Regression 370443.3236 18521.662 0.0000 Residual 14487.7627 308.2503 Total 49 51531.0863 Coefficients Standard Error t Stat P-value Intercept -5.5146 7.2273 -0.763 0.4493 Income 0.4262 0.0392 10.8668 0.0000 Size 5.5437 1.6949 3.2708 0.00020 a. What is the population model of this regression problem?b. What is the sample estimates of the regression problem?c. Which of the independent variables in the model are significant at the 5% level?…A manager at a local bank analyzed the relationship between monthly salary and three independent variables: Length of service (measured in months), Gender (0 = female, 1 = male), and Job type (0 = clerical, 1 = technical). The following ANOVA summarizes the regression results.ANOVA Source of Variation df Sum of Squares Mean Square F Regression 3 1,004,346.771 334,782.257 5.96 Residual 26 1,461,134.596 56,197.48445 Total 29 2,465,481.367 Coefficients Standard Error t-Stat p-value Intercept 784.92 322.25 2.44 0.02 Service 9.19 3.20 2.87 0.01 Gender 222.78 89.00 2.50 0.02 Job −28.21 89.61 −0.31 0.76 The level of significance is 0.05. Based on the hypothesis tests for the individual regression coefficients, ________. Multiple Choice all the regression coefficients are not equal to zero "Job" is the only significant variable in the model only months of service and gender are significantly related to monthly salary "Service"…
- A multiple regression analysis produced the following tables. Predictor Coefficients StandardErrort Statistic p-valueIntercept -139.609 2548.989 -0.05477 0.957154x 24.24619 22.25267 1.089586 0.295682x 32.10171 17.44559 1.840105 0.08869Source df SS MS F p-valueRegression 2 302689 151344.5 1.705942 0.219838Residual 13 1153309 88716.07Total 15 1455998Using = 0.01 to test the null hypothesis H :?1 = ?2 = 0, the critical F value is ____.6.701.964.845.995.70A multiple regression analysis produced the following tables. Predictor Coefficients StandardErrort Statistic p-valueIntercept 624.5369 78.49712 7.956176 6.88E-06x 8.569122 1.652255 5.186319 0.000301x 4.736515 0.699194 6.774248 3.06E-05Source df SS MS F p-valueRegression2 1660914 830457.1 58.319561.4E06Residual 11 156637.5 14239.77Total 13 1817552The adjusted R is ____________.0.91380.88910.88510.8981Dex Research Limited conducted a research to investigate consumer characteristics that can be used to predict the amount charged by credit card users. The following multiple regression output is based on a data collected by this research company on annual income, household size and annual credit card charges for a sample if 50 consumers. Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.9086 A 0.8181 398.0910 Observations ANOVA df SS MS Significance F Regression 2 1.50876E-18 Residual C 7448393.148 F Total 49 42699148.82 Coefficients Standard Error t Stat P-value Intercept 1304.9048 197.6548 6.6019 3.28664E-08 Income ($1000s) 33.1330 3.9679 H 7.68206E-11 Household Size 356.2959 33.2009 10.7315 3.12342E-14 a. Complete the missing entries from A to H in this output b. Estimate the annual credit card charges for a three-person household with an annual income of $40,000. C. Did the estimated regression equation provide a good fit to the data? Explain
- Dex Research Limited conducted a research to investigate consumer characteristics that can be used to predict the amount charged by credit card users. The following multiple regression output is based on a data collected by this research company on annual income, household size and annual credit card charges for a sample if 50 consumers. Regression Statistics Multiple R 0.9086 R Square A Adjusted R Square 0.8181 Standard Error 398.091 Observations B ANOVA df SS MS F Significance F Regression 2 C E G 1.50876E-18 Residual D 7448393 F Total 49 42699149 Coefficients Standard Error t Stat P-Value Intercept 1304.9048 197.6548 6.6019 3.28664E-08 Income ($1000s) 33.1330 3.9679 H 7.68206E-11 Household size 356.2959 33.2009 10.7315 3.12342E-14 a. Complete the missing entries from A to H in this outputb. Estimate the annual credit card charges…A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 2020 ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181 Total 1919 2078.6512078.651 Step 1 of 2: How many independent variables are included in the regression modelDex Research Limited conducted a research to investigate consumer characteristics that can be used to predict the amount charged by credit card users. The following multiple regression output is based on a data collected by this research company on annual income, household size and annual credit card charges for a sample if 50 consumers. Regression Statistics Multiple R 0.9086 R Square A Adjusted R Square 0.8181 Standard Error 398.091 Observations B ANOVA df SS MS F Significance F Regression 2 D E G 1.51E-18 Residual C 7448393 F Total 49 42699149 Coefficients Standard Error t Stat P-value Intercept 1304.9048 197.6548 6.6019 3.29E-08 Income ($1000s) 33.133 3.9679 H 7.68E-11 Household Size 356.2959 33.2009 10.7315 3.12E-14 a. Complete the missing entries from A to H in this output b. Estimate the annual credit card charges for a three-person household with an annual income of $40,000.c. Did the estimated regression…