Transcript Slide 1

Preparation for Final Exam
How to answer question related
to computer output?
1
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:
2
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
.
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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
3
Model Summary
Model Summary(b)
Model
1
R
R
Square
.787(a)
.619
Std. Error
Adjusted
of the
R Square Estimate
.603
.40376
DurbinWatson
1.633
a Predictors: (Constant), climate, self, gender, reciprocal
b Dependent Variable: intention
4
Test for the Model
ANOVA
Model
1
Sum of
Squares
b
df
Mean Square
Regression
24.131
4
6.033
Residual
14.835
91
.163
Total
38.966
95
F
37.006
Sig.
.000a
a. Predictors: (Constant), climate, s elf, gender, reciprocal
b. Dependent Variable: intention
5
Test for Individual Variables
Coefficients
Unstandardized
Coefficients
Model
1
B
a
Standardized
Coefficients
Std. Error
(Constant)
.379
.339
gender
.243
.096
reciprocal
.690
self
climate
Beta
Collinearity Statistics
t
Sig.
Tolerance
VIF
1.117
.267
.165
2.533
.013
.986
1.014
.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
a. Dependent Variable: intention
6
Assumptions (Multicollinearity)
Collinearity Diagnostics
Model
1
Dimens ion
1
Eigenvalue
Condition
Index
a
Variance Proportions
(Constant)
gender
reciprocal
self
climate
4.724
1.000
.00
.01
.00
.00
.00
2
.213
4.705
.00
.95
.00
.00
.01
3
.039
10.937
.00
.02
.02
.15
.61
4
.013
18.861
.41
.01
.77
.06
.00
5
.010
22.030
.58
.01
.21
.79
.37
a. Dependent Variable: intention
7
Data Normally Distributed
Histogram
Dependent Variable: intention
Frequency
40
30
20
10
0
-3
-2
-1
0
1
2
3
Mean = 1.17E-15
Std. Dev. = 0.979
N = 96
Regression Standardized Residual
8
Errors Normally Distributed
Normal P-P Plot of Regression Standardized Residual
Dependent Variable: Intention
Expected Cum Prob
1.0
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Observed Cum Prob
9
Outliers
Casewise Diagnostics(a)
Std.
Residual
45
56
Case Number
Intention
Predicted
Value
Residual
4.105
8.00
3.4824
1.51755
3.385
8.80
3.2813
1.43214
a Dependent Variable: Intention
10
Constant Variance (Homosdedasticity)
Scatterplot
Dependent Variable: intention
Regression Studentized
Residual
3
2
1
0
-1
-2
-3
-4
-3
-2
-1
0
1
2
3
Regression Standardized Predicted Value
11
Linearity
Partial Regression Plot
Dependent Variable: intention
intention
1
0
-1
-2
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
reciprocal
12
Linearity
Partial Regression Plot
Dependent Variable: intention
1.5
intention
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
self
13
Linearity
Partial Regression Plot
Dependent Variable: intention
1.0
intention
0.5
0.0
-0.5
-1.0
-2
-1
0
1
2
climate
14
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
6.58
8.15
.
10.89
10.38
10.39
.
584
483
484
.
9
7
4
.
.
.
8.27
.
.
11.02
.
.
636
.
.
4
7.57
8.5
10.72
10.22
515
636
4
4
16
Model Summary
Model Summary
Model
1
R
R Square
.699a
b
Adjusted
R Square
.488
Std. Error of
the Estimate
.472
.7541
Durbin-W
atson
2.088
a. Predictors: (Constant), Pengalaman berkerja, Graduate
Managament Aptitude Test, GPA semasa Ijazah Pertama
b. Dependent Variable: GPA kursus MBA
17
Test for the Model
ANOVA b
Model
1
Sum of
Squares
df
Mean Square
Regression
52.063
3
17.354
Residual
54.594
96
.569
106.656
99
Total
F
30.516
Sig.
.000a
a. Predictors: (Constant), Pengalaman berkerja, Graduate Managament Aptitude Test,
GPA semasa Ijazah Pertama
b. Dependent Variable: GPA kursus MBA
18
Test for Individual Variables
Coefficients
Standardi
zed
Coefficien
ts
Unstandardized
Coefficients
Model
1
B
(Constant)
a
Std. Error
.530
1.323
GPA semasa Ijazah
Pertama
8.236E-02
.105
Graduate Managament
Aptitude Test
1.092E-02
Pengalaman berkerja
9.275E-02
Beta
Collinearity Statistics
t
Sig.
Tolerance
VIF
.401
.689
.057
.782
.436
.988
1.013
.001
.622
8.505
.000
.997
1.003
.022
.310
4.225
.000
.990
1.010
a. Dependent Variable: GPA kursus MBA
19
Assumptions
Collinearity Diagnostics
a
Variance Proportions
Model
1
Eigenvalue
3.738
Condition
Index
1.000
(Constant)
.00
GPA semasa
Ijazah
Pertama
.00
2
.252
3.854
.00
.00
.00
.98
3
8.437E-03
21.048
.02
.20
.75
.00
4
2.063E-03
42.569
.98
.79
.25
.00
Dimension
1
Graduate
Managament
Aptitude Test
.00
Pengalaman
berkerja
.02
a. Dependent Variable: GPA kursus MBA
20
Data Normally Distributed
Histogram
Dependent Variable: GPA kursus MBA
14
12
10
8
6
4
Std. Dev = .98
2
Mean = 0.00
0
N = 100.00
2
2
1
1
1
1
.2
.0
.7
.5
.2
.0
5
0
5
0
5
0
5
.7
0
.5
5
.2
0
.0
0
5
2
-.
0
5
-.
5
7
-. 0
.0
-1
5
.2
-1 0
.5
-1
5
.7
-1 0
.0
5
.2
-2
-2
Regr es s ion Standar dized Res idual
21
Exp e cte d C u m P ro b
Errors Normally Distributed
Normal P-P Plot of Regression Standardized Residual
Dependent Variable: GPA kursus MBA
1.00
.75
.50
.25
0.00
0.00
.25
.50
.75
1.00
Observ ed Cum Prob
22
Outliers
Casewise Diagnostics(a)
Std.
Residual
GPA Kursus
MBA
Predicted
Value
Residual
45
4.105
8.00
3.4824
1.51755
56
3.385
8.80
3.2813
1.43214
Case Number
a Dependent Variable: GPA Kursus MBA
23
Constant Variance (Homosdedasticity)
GPA Kursus MBA
24
Linearity
GPA Kursus MBA
G
P
A
M
B
A
GMAT
25