1 preparation for final exam how to answer question related to computer output?
TRANSCRIPT
1
Preparation for Final Exam
How to answer question related to computer output?
2
Multiple Regression
Ahmad a manager at a university feels that lecturers ought to have high intentions to share information among them so that the competitiveness of the university can be enhanced. Lately there have been many complaints about the poor quality of students from the public institutions of higher learning. Ahmad has collected data from 96 lecturers and the variables that has been emphasized are Gender (gender of the respondent 1 = Male, 0 = Female), Reciprocal (Reciprocal relationship between lecturers), Self (self efficacy), Climate (Climate of the organization) and Intention (Intention to share information). The data was analysed using SPSS and the output is given below:
3
Data
ID Reciprocal Self Climate Gender Intention
1 3.60 4.00 4.33 1 3.60
2 3.60 4.00 3.50 0 3.20
3 2.80 4.00 3.00 1 4.00
. . . . . .
. . . . . .
. . . . . .
94 2.00 2.00 2.17 1 5.00
95 3.80 5.00 2.67 0 4.60
96 3.80 5.00 3.50 1 4.00
4
Model Summary
Model Summary(b)
Model RR
SquareAdjusted R Square
Std. Error of the
EstimateDurbin-Watson
1 .787(a) .619 .603 .40376 1.633
a Predictors: (Constant), climate, self, gender, reciprocalb Dependent Variable: intention
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Test for the Model
ANOVAb
24.131 4 6.033 37.006 .000a
14.835 91 .16338.966 95
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), climate, self, gender, reciprocala.
Dependent Variable: intentionb.
6
Test for Individual Variables
Coefficientsa
.379 .339 1.117 .267
.243 .096 .165 2.533 .013 .986 1.014
.690 .092 .607 7.528 .000 .644 1.553
.231 .079 .229 2.927 .004 .685 1.460
.022 .060 .025 .370 .712 .882 1.134
(Constant)genderreciprocalselfclimate
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: intentiona.
7
Assumptions (Multicollinearity)
Collinearity Diagnosticsa
4.724 1.000 .00 .01 .00 .00 .00.213 4.705 .00 .95 .00 .00 .01.039 10.937 .00 .02 .02 .15 .61.013 18.861 .41 .01 .77 .06 .00.010 22.030 .58 .01 .21 .79 .37
Dimension12345
Model1
EigenvalueCondition
Index (Constant) gender reciprocal self climateVariance Proportions
Dependent Variable: intentiona.
8
Data Normally Distributed
3210-1-2-3
Regression Standardized Residual
40
30
20
10
0
Fre
qu
en
cy
Mean = 1.17E-15Std. Dev. = 0.979N = 96
Dependent Variable: intention
Histogram
9
Errors Normally Distributed
1.00.80.60.40.20.0
Observed Cum Prob
1.0
0.8
0.6
0.4
0.2
0.0
Exp
ecte
d C
um
Pro
bDependent Variable: Intention
Normal P-P Plot of Regression Standardized Residual
10
Outliers
Casewise Diagnostics(a)
1.432143.28138.803.38556
1.517553.48248.004.10545
ResidualPredicted
ValueIntentionStd.
ResidualCase Number
a Dependent Variable: Intention
11
Constant Variance (Homosdedasticity)
3210-1-2-3
Regression Standardized Predicted Value
3
2
1
0
-1
-2
-3
-4
Re
gre
ss
ion
Stu
de
nti
ze
d
Re
sid
ua
l
Dependent Variable: intention
Scatterplot
12
Linearity
1.51.00.50.0-0.5-1.0-1.5
reciprocal
1
0
-1
-2
inte
nti
on
Dependent Variable: intention
Partial Regression Plot
13
Linearity
1.00.50.0-0.5-1.0-1.5-2.0
self
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
inte
nti
on
Dependent Variable: intention
Partial Regression Plot
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Linearity
210-1-2
climate
1.0
0.5
0.0
-0.5
-1.0
inte
nti
on
Dependent Variable: intention
Partial Regression Plot
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Multiple Regression
The MBA was started in USM since 1995 and the dean would like to analyze the factors that influence the performance of the candidates. One hundred students who have graduated the last three years were selected and their GPA during their first degree, GMAT score and their number of years working experience before they joined were recorded. The data was analyzed using the SPSS software and the output is presented below:
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Data
MBAGPA UNDERGPA GMAT WORK
8.43 10.89 584 9
6.58 10.38 483 7
8.15 10.39 484 4
. . . .
. . . .
. . . .
8.27 11.02 636 4
7.57 10.72 515 4
8.5 10.22 636 4
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Model Summary
Model Summaryb
.699a .488 .472 .7541 2.088Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Durbin-Watson
Predictors: (Constant), Pengalaman berkerja, GraduateManagament Aptitude Test, GPA semasa Ijazah Pertama
a.
Dependent Variable: GPA kursus MBAb.
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Test for the Model
ANOVAb
52.063 3 17.354 30.516 .000a
54.594 96 .569
106.656 99
Regression
Residual
Total
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), Pengalaman berkerja, Graduate Managament Aptitude Test,GPA semasa Ijazah Pertama
a.
Dependent Variable: GPA kursus MBAb.
19
Test for Individual Variables
Coefficientsa
.530 1.323 .401 .689
8.236E-02 .105 .057 .782 .436 .988 1.013
1.092E-02 .001 .622 8.505 .000 .997 1.003
9.275E-02 .022 .310 4.225 .000 .990 1.010
(Constant)
GPA semasa IjazahPertama
Graduate ManagamentAptitude Test
Pengalaman berkerja
Model1
B Std. Error
UnstandardizedCoefficients
Beta
Standardized
Coefficients
t Sig. Tolerance VIF
Collinearity Statistics
Dependent Variable: GPA kursus MBAa.
20
Assumptions
Collinearity Diagnosticsa
3.738 1.000 .00 .00 .00 .02
.252 3.854 .00 .00 .00 .98
8.437E-03 21.048 .02 .20 .75 .00
2.063E-03 42.569 .98 .79 .25 .00
Dimension1
2
3
4
Model1
EigenvalueCondition
Index (Constant)
GPA semasaIjazah
Pertama
GraduateManagamentAptitude Test
Pengalamanberkerja
Variance Proportions
Dependent Variable: GPA kursus MBAa.
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Data Normally Distributed
Regression Standardized Residual
Histogram
Dependent Variable: GPA kursus MBA
Fre
qu
en
cy
14
12
10
8
6
4
2
0
Std. Dev = .98
Mean = 0.00
N = 100.00
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Errors Normally Distributed
Normal P-P Plot of Regression Standardized Residual
Dependent Variable: GPA kursus MBA
Observed Cum Prob
1.00.75.50.250.00
Exp
ecte
d C
um
Pro
b
1.00
.75
.50
.25
0.00
23
Outliers
Casewise Diagnostics(a)
1.432143.28138.803.38556
1.517553.48248.004.10545
ResidualPredicted
ValueGPA Kursus
MBAStd.
ResidualCase Number
a Dependent Variable: GPA Kursus MBA
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Constant Variance (Homosdedasticity)
GPA Kursus MBA
25
Linearity
GPA Kursus MBA
GMAT
GPA
MBA