Question

] 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)

Answer #1

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...

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...

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?

SUMMARY OUTPUT Regression Statistics Multiple R 0.84508179 R
Square 0.714163232 Adjusted R Square 0.704942691 Standard Error
9.187149383 Observations 33 ANOVA df SS MS F Significance F
Regression 1 6537.363661 6537.363661 77.4535073 6.17395E-10
Residual 31 2616.515127 84.40371378 Total 32 9153.878788
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Lower 95.0% Upper 95.0% Intercept 61.07492285 3.406335763
17.92980114 6.41286E-18 54.12765526 68.02219044 54.12765526
68.02219044 Time (Y) -0.038369095 0.004359744 -8.800767426
6.17395E-10 -0.047260852 -0.029477338 -0.047260852 -0.029477338
Using your highlighted cells, what is the equation...

Consider the following computer output of a multiple regression
analysis relating annual salary to years of education and years of
work experience.
Regression Statistics
Multiple R
0.7345
R Square
0.5395
Adjusted R Square
0.5195
Standard Error
2134.9715
Observations
49
ANOVA
df
SS
MS
F
Significance F
Regression
2
245,644,973.9500
122,822,486.9750
26.9460
1.8E-08
Residual
46
209,672,760.0092
4,558,103.4785
Total
48
455,317,733.9592
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
14271.51879
2,525.5672
5.6508
0.000000963
9187.8157
19,355.2219
Education (Years)
2351.3035...

Consider the following computer output of a multiple regression
analysis relating annual salary to years of education and years of
work experience.
Regression Statistics
Multiple R
0.7338
R Square
0.5384
Adjusted R Square
0.5183
Standard Error
2139.0907
Observations
49
ANOVA
df
SS
MS
F
Significance F
Regression
2
245,472,093.5833
122,736,046.7917
26.8234
1.9E-08
Residual
46
210,482,624.6208
4,575,709.2309
Total
48
455,954,718.2041
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
14275.75637
2,530.4400
5.6416
0.000000994
9182.2448
19,369.2679
Education (Years)
2350.2675
338.3625...

Calculate the following statistics given the existing
information (1 point per calculation):
Regression Statistics
Multiple R
R Square
Adjusted R Square
0.559058
Standard Error
Observations
30
ANOVA
df
SS
MS
F
Significance F
Regression
2
3609132796
19.38411515
6.02827E-06
Residual
27
2513568062
Total
29
6122700857
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
-15800.8
57294.51554
-0.27578
0.784814722
CARAT
12266.83
1999.250369
6.135715
1.48071E-06
DEPTH
156.686
928.9461882
0.168671
0.867312915
Additionally interpret your results. Be sure to comment on
Accuracy, significance...

1. For the following multiple regression which was
conducted to attempt to predict the variable based on the
independent variables shown, answer the following
questions.
Regression Statistics
Multiple R
0.890579188
R Square
0.793131289
Adjusted R Square
0.7379663
Standard Error
30.28395534
Observations
20
ANOVA
df
SS
MS
F
Regression
4
52743.23074
13185.81
14.37743932
Residual
15
13756.76926
917.1179509
Total
19
66500
Coefficients
Standard Error
t Stat
P-value
Intercept
73.33291
62.25276
1.17799
0.25715
X1
-0.13882
0.05353
-2.59326
0.02037
X2
3.73984
0.95568
3.91328
0.00138...

Below you are given a partial Excel output based on a sample of
16 observations.
ANOVA
df
SS
MS
F
Regression
4,853
2,426.5
Residual
485.3
Coefficients
Standard Error
Intercept
12.924
4.425
x1
-3.682
2.630
x2
45.216
12.560
?
?
Refer to Exhibit 13-6. Carry out the test of significance for
the parameter ?1 at the 1% level. The null hypothesis
should be
Select one:
a.
None of these alternatives is correct.
b.
revised
c.
rejected
d.
not rejected

Compare the two regression models. Does it make sense that
spending and household debt could each be predicted by annual
household income? Why or why not?
1. Predicting spending by household income
Regression
Statistics
Multiple R
0.859343186
R Square
0.738470711
Adjusted R
Square
0.737149856
Standard Error
1602.157625
Observations
200
ANOVA
df
SS
MS
F
Significance
F
Regression
1
1435121315
1435121315
559.085376
1.42115E-59
Residual
198
508247993.2
2566909.056
Total
199
1943369308
Coefficients
Standard Error
t
Stat
P-value
Lower 95%
Upper 95%
Lower...

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