. The administrator of a new paralegal program at Seagate Technical College wants to estimate the grade point average (GPA) in the new program. He thought that high school GPA, the verbal score on the Scholastic Aptitude Test (SAT), and the mathematics score on the SAT would be good predictors of paralegal GPA. The data on 9 students are: SATMoth ol

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12. The administrator of a new paralegal program at Seagate Technical College wants to
estimate the grade point average (GPA) in the new program. He thought that high school
GPA, the verbal score on the Scholastic Aptitude Test (SAT), and the mathematics score
on the SAT would be good predictors of paralegal GPA. The data on 9 students are:
High School SAT Verbal
GPA
Student
SAT Math
Paralegal
GPA
3.25
480
410
3.21
2
1.80
290
270
1.68
2.89
420
410
3.58
3.81
500
600
3.92
5
3.13
500
490
3.00
2.81
430
460
2.82
7
2.20
320
490
1.65
8.
2.14
530
480
2.30
2.63
469
440
2.33
Considering the following output.
· reg paralegalgpa highschoolgpa satverbal satmath
Source
df
MS
Number of obs =
F( 3,
5) =
10.33
Model
4.3594607
1.45315357
Prob > F
0.0139
Residual
703628528
.140725706
R-squared
0.8610
Adj R-squared =
0.7776
Total
5.06308922
8.
.632886153
Root MSE
.37513
paralegalgpa
Coef.
Std. Err.
P>|t|
[958 Conf. Interval]
t
highschoolgpa
1.201374
.295513
4.07
0.010
.4417341
1.961015
satverbal
.0016293
.0021471
0.76
0.482
-.0038901
.0071487
satmath
-.0019393
.0020736
-0.94
0.393
-.0072697
.0033912
-.4110716
.7823005
-0.53
0.622
-2.422039
1.599896
cons
a. Global test of hypothesis: Does it appear that any of the regression coefficients are
not equal to zero? Explain or show your reasoning.
Transcribed Image Text:12. The administrator of a new paralegal program at Seagate Technical College wants to estimate the grade point average (GPA) in the new program. He thought that high school GPA, the verbal score on the Scholastic Aptitude Test (SAT), and the mathematics score on the SAT would be good predictors of paralegal GPA. The data on 9 students are: High School SAT Verbal GPA Student SAT Math Paralegal GPA 3.25 480 410 3.21 2 1.80 290 270 1.68 2.89 420 410 3.58 3.81 500 600 3.92 5 3.13 500 490 3.00 2.81 430 460 2.82 7 2.20 320 490 1.65 8. 2.14 530 480 2.30 2.63 469 440 2.33 Considering the following output. · reg paralegalgpa highschoolgpa satverbal satmath Source df MS Number of obs = F( 3, 5) = 10.33 Model 4.3594607 1.45315357 Prob > F 0.0139 Residual 703628528 .140725706 R-squared 0.8610 Adj R-squared = 0.7776 Total 5.06308922 8. .632886153 Root MSE .37513 paralegalgpa Coef. Std. Err. P>|t| [958 Conf. Interval] t highschoolgpa 1.201374 .295513 4.07 0.010 .4417341 1.961015 satverbal .0016293 .0021471 0.76 0.482 -.0038901 .0071487 satmath -.0019393 .0020736 -0.94 0.393 -.0072697 .0033912 -.4110716 .7823005 -0.53 0.622 -2.422039 1.599896 cons a. Global test of hypothesis: Does it appear that any of the regression coefficients are not equal to zero? Explain or show your reasoning.
b. Test of hypothesis on each independent variable. Would you consider eliminating
the variables “verbal" and "math"? Let a = 0.05. Explain or show your reasoning.
reg paralegalgpa highschoolgpa
Source
df
MS
Number of obs -
F( 1,
7) -
34.35
Model
4.2060623
1
4.2060623
Prob > F
0.0006
Residual
.857026927
7
.122432418
R-squared.
0.8307
Adj R-squared -
0.8065
Total
5.06308922
8
.632886153
Root MSE
.3499
paralegalgpa
Coef.
Std. Err.
P>|t|
[95% Conf. Interval)
highschoolgpa
1.158886
.1977203
5.86
0.001
.6913522
1.626421
_cons
-.4542376
.5541665
-0.82
0.439
-1.764633
.856158
c. The analysis has been rerun without “verbal" and "math". See the above output.
How much has R-squared and adjusted R-squared changed from the previous
analysis?
d. Why do we prefer to use adjusted R-squared to measure goodness-of-fit of a
multiple regression model?
Transcribed Image Text:b. Test of hypothesis on each independent variable. Would you consider eliminating the variables “verbal" and "math"? Let a = 0.05. Explain or show your reasoning. reg paralegalgpa highschoolgpa Source df MS Number of obs - F( 1, 7) - 34.35 Model 4.2060623 1 4.2060623 Prob > F 0.0006 Residual .857026927 7 .122432418 R-squared. 0.8307 Adj R-squared - 0.8065 Total 5.06308922 8 .632886153 Root MSE .3499 paralegalgpa Coef. Std. Err. P>|t| [95% Conf. Interval) highschoolgpa 1.158886 .1977203 5.86 0.001 .6913522 1.626421 _cons -.4542376 .5541665 -0.82 0.439 -1.764633 .856158 c. The analysis has been rerun without “verbal" and "math". See the above output. How much has R-squared and adjusted R-squared changed from the previous analysis? d. Why do we prefer to use adjusted R-squared to measure goodness-of-fit of a multiple regression model?
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