Question

QUESTION 17 The following results are from data where the dependent variable is the selling price of used cars, the independent variables are similar to those in the above regression along with some additional variables. The data were split into 2 samples and the following regression results were obtained from the split data. SUMMARY OUTPUT Regression Statistics Multiple R 0.846 R Square 0.715 Adjusted R Square 0.653 Standard Error 872.9 Observations 49 ANOVA df SS MS F Significance F Regression 9 76575468 8508385 11.2 1.03933E-08 Residual 40 30479336 761983 Total 49 107054804 Coefficients Standard Error t Stat P-value Intercept 17270.81 2684.71 6.43 0.00 MSRP_Amt 0.35 0.06 5.55 0.00 Manual 384.14 309.31 1.24 0.22 Convertible -814.13 375.34 -2.17 0.04 VIN8_6Cylinder -637.40 452.57 -1.41 0.17 Mileage_Sale -64.41 14.82 -4.35 0.00 ModelYearAge_Months -276.45 41.09 -6.73 0.00 CondReptAmt -0.50 0.49 -1.02 0.31 RepairAmt 1.52 0.70 2.17 0.04 InventoryAge_Days -7.91 10.17 -0.78 0.44 SUMMARY OUTPUT Regression Statistics Multiple R 0.724 R Square 0.524 Adjusted R Square 0.423 Standard Error 1622.0 Observations 52 ANOVA df SS MS F Significance F Regression 9 116147955 12905328 4.90 0.000104741 Residual 40 105247338 2631183 Total 49 221395293 Coefficients Standard Error t Stat P-value Intercept 13992.20 3169.78 4.41 0.00 MSRP_Amt 0.19 0.07 2.72 0.01 Manual 629.14 459.84 1.37 0.18 Convertible -920.90 1060.78 -0.87 0.39 VIN8_6Cylinder -1251.81 752.26 -1.66 0.10 Mileage_Sale -49.71 13.24 -3.75 0.00 ModelYearAge_Months -153.04 55.28 -2.77 0.01 CondReptAmt -0.49 0.32 -1.53 0.13 RepairAmt -1.09 1.09 -0.99 0.33 InventoryAge_Days -19.88 7.84 -2.54 0.01 If we wanted to test for heteroscedasticity, what is the test statistic? (please round your answer to 2 decimal places)

Answer #1

According to the Data, is the regression a better fit than the
one with the Dummy variable, explain?
Regression Statistics
Multiple R
0.550554268
R Square
0.303110002
Adjusted R Square
0.288887757
Standard Error
2.409611727
Observations
51
ANOVA
df
SS
MS
F
Significance F
Regression
1
123.7445988
123.7445988
21.31238807
2.8414E-05
Residual
49
284.5052051
5.806228676
Total
50
408.2498039
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Intercept
5.649982553
1.521266701
3.713998702
0.000522686
2.592882662
U-rate
1.826625993
0.395670412
4.616534206
2.84144E-05
1.0314965
Multiple R
0.572568188
R Square...

] A partial computer output from a regression analysis using
Excel’s Regression tool follows. Regression Statistics Multiple R
(1) R Square 0.923 Adjusted R Square (2) Standard Error 3.35
Observations ANOVA df SS MS F Significance F Regression (3) 1612
(7) (9) Residual 12 (5) (8) Total (4) (6) Coefficients Standard
Error t Stat P-value Intercept 8.103 2.667 x1 7.602 2.105 (10) x2
3.111 0.613 (11)

QUESTION 3
The managing director of a real estate company investigated how
advertising budget (in $000s) and number of agents affected annual
sales ($ million). He used data from 15 offices, and obtained the
following regression output:
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.72
R Square
0.52
Adjusted R Square
0.44
Standard Error
7.36
Observations
15
ANOVA
df
SS
MS
F
Significance
Regression
2
716.58
358.29
6.61
0.01
Residual
12
650.35
54.20
Total
14
1366.93
Coefficients
Standard Error
t Stat...

SF = square feet of the house
PY: 0 = no pool, 1 = pool
BY: 0 = not made of bricks, 1 made of bricks
There are three neighborhoods N1, N2 and N3
y = house price
Regression Statistics
Multiple R
0.91
R Square
0.84
Adjusted R Square
0.83
Standard Error
20,949.15
Observations
301.00
ANOVA
df
SS
MS
F
Significance F
Regression
4.00
667,427,645,418.08
166,856,911,354.52
380.20
0.00
Residual
296.00
129,904,621,849.66
438,866,965.71
Total
300.00
797,332,267,267.74
Coefficients
Standard Error
t Stat...

Multiple R=0.81112189
R Square=0.65791872
Adj. R Square=0.61515856
Standard Error=11.6506589
Observations=10
Regression: df=1 ss=2088.497175 ms=2088.497 F=15.3863
Residual: df=8 ss=1085.902825 ms=135.7379
Total: df=9 ss=3174.4
Intercept: Coefficients=-70.39 Std. Error=30.00
P-value=0.047
X: Coefficients=17.18 Std. Error=4.38 t-stat=3.92 p
value=0.004
Question: For the test of hypothesis regarding
the intercept of the model, compute and report the
calculated value of the test-statistic.
Question: Predict value of Y, when X = 10.

interpret each coefficient estimate and discuss its significance
using α = 0.01, α = 0.05 and α = 0.10. Use the concepts of strict
and weak significance too
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.820140129
R
Square
0.672629832
Adjusted R Square
0.658699186
Standard Error
235.4076294
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
2
5351505.158
2675752.58
48.2841827
4.0125E-12
Residual
47
2604587.342
55416.752
Total
49
7956092.5
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper...

Solve the missing values in the following regression model.
Write down all solutions along with their key letter.
Regression Statistics
Multiple R
0.489538
R Square
0.239648
Adjusted R Square
0.231889
Standard Error
11.76656
Observations
100
ANOVA
df
SS
MS
F
Significance F
Regression
1
4276.457
30.88765
2.35673E-07
Residual
138.452
Total
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 99.0%
Upper 99.0%
Intercept
-24.1551
12.83013
-1.88268
0.062709
-49.61605579
1.305895
-57.8589
9.548787
Food
3.167042
0.569851
2.36E-07
2.03619109
4.297893
1.670083...

Using the attached regression output, answer the
following:
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.972971
R Square
0.946673
Adjusted R Square
0.944355
Standard Error
76.07265
Observations
49
ANOVA
df
SS
MS
F
Significance F
Regression
2
4725757
2362878
408.3046
5.24E-30
Residual
46
266204.2
5787.049
Total
48
4991961
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
-0.46627
14.97924
-0.03113
0.975302
-30.6179
29.68537
X1
0.09548
0.084947
1.123997
0.266846
-0.07551
0.26647
X2
0.896042
0.205319
4.364141
7.16E-05
0.482756
1.309328
a. What...

Regression Statistics
Multiple
R
0.3641
R
Square
0.1325
Adjusted
R Square
0.1176
Standard
Error
0.0834
Observations
60
ANOVA
df
SS
MS
F
Significance F
Regression
1
0.0617
0.0617
8.8622
0.0042
Residual
58
0.4038
0.0070
Total
59
0.4655
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
-0.0144
0.0110
-1.3062
0.1966
-0.0364
0.0077
X
Variable 1
0.8554
0.2874
2.9769
0.0042
0.2802
1.4307
How do you interpret the above table?

True or false: at the 5% level of confidence the intercept is
significantly different from zero? SUMMARY OUTPUT Regression
Statistics Multiple R 0.98711 R Square 0.974387 Adjusted R Square
0.965849 Standard Error 47.4523 Observations 9 ANOVA df SS MS F
Significance F Regression 2 513960.7 256980.4 114.1262 1.68E-05
Residual 6 13510.32 2251.72 Total 8 527471.1 Coefficients Standard
Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -100.805 48.43281 -2.08133 0.082583 -219.316 17.70612
-219.316 17.70612 Well Depth...

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