AGE OF RESPONDENT, HIGHEST YEAR OF SCHOOL COMPLETEDb
.
Enter
a. Dependent Variable: R’s socioeconomic index (2010)
b. All requested variables entered.
Model Summaryb
Model
R
R Square
Adjusted R
Square
Std. Error
of the Estimate
Durbin-Watson
1
.599a
.359
.358
17.9504
1.955
a. Predictors: (Constant), AGE OF RESPONDENT, HIGHEST YEAR OF
SCHOOL COMPLETED
b. Dependent Variable: R’s socioeconomic index (2010)
ANOVAa
Model
Sum of
Squares
df
Mean
Square
F
Sig.
1
Regression
435840.770
2
217920.385
676.314
.000b
Residual
778800.487
2417
322.218
Total
1214641.257
2419
a. Dependent Variable: R’s socioeconomic index (2010)
b. Predictors: (Constant), AGE OF RESPONDENT, HIGHEST YEAR OF
SCHOOL COMPLETED
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
Collinearity
Statistics
B
Std. Error
Beta
Tolerance
VIF
1
(Constant)
-22.552
2.009
-11.227
.000
HIGHEST YEAR OF SCHOOL COMPLETED
4.287
.120
.584
35.863
.000
.999
1.001
AGE OF RESPONDENT
.193
.021
.148
9.095
.000
.999
1.001
a. Dependent Variable: R’s socioeconomic index (2010)
Collinearity
Diagnosticsa
Model
Dimension
Eigenvalue
Condition
Index
Variance Proportions
(Constant)
HIGHEST
YEAR OF SCHOOL COMPLETED
AGE OF
RESPONDENT
1
1
2.894
1.000
.00
.01
.01
2
.085
5.830
.02
.17
.81
3
.020
11.890
.97
.83
.18
a. Dependent Variable: R’s socioeconomic index (2010)
Residuals
Statisticsa
Minimum
Maximum
Mean
Std.
Deviation
N
Predicted Value
-18.302
80.195
46.004
13.4229
2420
Std. Predicted Value
-4.791
2.547
.000
1.000
2420
Standard Error of Predicted Value
.366
1.797
.605
.183
2420
Adjusted Predicted Value
-18.932
80.148
46.001
13.4316
2420
Residual
-48.8160
79.8744
.0000
17.9430
2420
Std. Residual
-2.719
4.450
.000
1.000
2420
Stud. Residual
-2.722
4.470
.000
1.000
2420
Deleted Residual
-48.9185
80.5965
.0027
17.9686
2420
Stud. Deleted Residual
-2.726
4.487
.000
1.001
2420
Mahal. Distance
.007
23.254
1.999
2.177
2420
Cook’s Distance
.000
.060
.000
.002
2420
Centered Leverage Value
.000
.010
.001
.001
2420
a. Dependent Variable: R’s socioeconomic index (2010)
What is your research question?
Respondents’ highest level of school completed and their age may possibly cause
an impact in their level of socioeconomic index (2010).
Interpret the coefficients for the model,
specifically commenting on the dummy variable. I am estimating a multiple
regression model using respondent’s socioeconomic status index as the dependent
variable, respondent’s highest year of education as an independent variable,
and the age of respondents as an independent variable. Our coefficient table
tells us more information about individual independent variables. Another
important consideration to look into is the variance inflation factor. VIF is
the number that shows the level of severity of multicollinearity in an ordinary
least squares regression analysis (Warner, 2012). Values within 10 and above 10
indicate serious multicollinearity or high probability of correlation in the
model. However, 1.001 for both the predictors indicate normal level of
correspondence or assumption.
Run diagnostics for the regression model.
Does the model meet all of the assumptions? Be sure and comment on what
assumptions were not met and the possible implications. Is there any possible
remedy for one the assumption violations? After analyzing and reviewing all
tables and data of the applicable variables, there seem to exhibit no possible
violations on the assumptions of the resulted data. Therefore, all assumptions
were possibly made. In my Model Summary table, the Durbin-Watson statistic,
which tells us about the independence of errors (Laureate Education, 2016j ),
is showing a value of 1.955. This value is an example of an absolute absent of
correlation between the residuals (Laureate Education, 2016j). The Anova table
is showing the overall statistical significant of the calculated variables. In
this case, we have a statistical significant of 0.000, indicating the rejection
of the null hypothesis when conventional P-value is set to P<0.05.Our Cook’s
distance shows an unnecessary relationship on the model ranging from 0.0-
0.025, with value of 1.0 or greater showing possible influence of
correlation. Our scatter plot provides
uniformities of display of homoscedasticity, and give more details about
linearity relationship (Wagner, 2016). The histogram indicates how the
distribution of correlation or no errors exists. Looking at the histogram, the
distribution display of the frequency and regression standardized residual
shows an insignificant deviation from normalcy.
Reference
Laureate Education
(Producer). (2016j). Regression
diagnostics, model evaluation, and dummy variables [Video file]. Baltimore,
MD: Author.
Wagner,
W. E. (2016). Using IBM® SPSS® statistics
for research methods and social science statistics (6th ed.). Thousand
Oaks, CA: Sage Publications
Warner,
R. M. (2012). Applied Statistics from
bivariate through multivariate techniques (2nd ed.). Thousand
Oaks, CA: Sage Publications.
