Based on the information provided below, please interpret each table and what th
ID: 3270085 • Letter: B
Question
Based on the information provided below, please interpret each table and what the conclusion is based on SPSS tables.
Introduction : In today’s age, the need for quality education has increased more than ever. And the level of communication happening globally, has brought to light the ideal circumstances of teaching, adapted to different environments. One of the variables that affects the quality of education of the students, can be, the quality of education of the teachers. We wish to find this out.
Statement of the problem : The issue of low quality of education for some special ed students, needs to be correlated with the factor(s) behind the same, so that the situation can be improved.
Purpose of the study: To find the relationship(or lack thereof) between the education (weighted by age) of teachers & the education of special ed students.
Research question : Does more education equate to better satifaction in the classroom or does education impact classroom satisfaction?
Ho : Education of teachers who work with special ed students does not equate to better satisfaction in the classroom.
H1 : Education of teachers who work with special ed students DOES equate to better satisfaction in the classroom.
Ho : There is no statistically significant correlation between age and education.
H1 : There is a statistically significant correlation between age and education.
Correlations
education
age
education
Pearson Correlation
1
.557**
Sig. (2-tailed)
.000
N
50
50
Bootstrapc
Bias
0
-.006
Std. Error
0
.144
95% Confidence Interval
Lower
1
.259
Upper
1
.803
age
Pearson Correlation
.557**
1
Sig. (2-tailed)
.000
N
50
50
Bootstrapc
Bias
-.006
0
Std. Error
.144
0
95% Confidence Interval
Lower
.259
1
Upper
.803
1
**. Correlation is significant at the 0.01 level (2-tailed).
c. Unless otherwise noted, bootstrap results are based on 50 bootstrap samples
If p>.05 we fail to reject the Ho. Therefore the Null stands and the Ha is rejected.
Bootstrap Specifications
Sampling Method
Simple
Number of Samples
50
Confidence Interval Level
95.0%
Confidence Interval Type
Percentile
Regression
Descriptive Statistics
Statistic
Bootstrapa
Bias
Std. Error
95% Confidence Interval
Lower
Upper
education
Mean
1.300
-.008
.079
1.147
1.493
Std. Deviation
.5803
-.0195
.0787
.3686
.7232
N
50
0
0
50
50
CPS1
Mean
4.2600
.0084
.2620
3.7268
4.8065
Std. Deviation
1.90392
-.03465
.12599
1.62361
2.11000
N
50
0
0
50
50
CPS2
Mean
3.8600
.0472
.2972
3.1400
4.4857
Std. Deviation
1.95886
-.01041
.12360
1.66711
2.20459
N
50
0
0
50
50
CPS3
Mean
3.7000
-.0124
.2491
3.1413
4.1932
Std. Deviation
1.77569
-.03691
.11507
1.51834
2.00658
N
50
0
0
50
50
CPS4
Mean
3.9800
.0448
.2834
3.5668
4.7465
Std. Deviation
1.94296
-.02837
.14251
1.61089
2.26370
N
50
0
0
50
50
CPS5
Mean
4.1200
-.0264
.2827
3.4215
4.6262
Std. Deviation
2.20056
-.02578
.12692
1.84049
2.43230
N
50
0
0
50
50
a. Unless otherwise noted, bootstrap results are based on 50 bootstrap samples
Model Summaryb
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
Durbin-Watson
1
.411a
.169
.074
.5583
1.681
a. Predictors: (Constant), CPS5, CPS3, CPS4, CPS2, CPS1
b. Dependent Variable: education
Bootstrap for Model Summary
Model
Durbin-Watson
Bootstrapa
Bias
Std. Error
95% Confidence Interval
Lower
Upper
1
1.681
-.346
.268
.775
1.912
a. Unless otherwise noted, bootstrap results are based on 50 bootstrap samples
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
2.784
5
.557
1.786
.135b
Residual
13.716
44
.312
Total
16.500
49
a. Dependent Variable: education
b. Predictors: (Constant), CPS5, CPS3, CPS4, CPS2, CPS1
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
95.0% Confidence Interval for B
Correlations
Collinearity Statistics
B
Std. Error
Beta
Lower Bound
Upper Bound
Zero-order
Partial
Part
Tolerance
VIF
1
(Constant)
2.026
.419
4.835
.000
1.181
2.870
CPS1
-.041
.044
-.136
-.933
.356
-.131
.048
-.072
-.139
-.128
.887
1.127
CPS2
.044
.043
.149
1.033
.307
-.042
.130
.110
.154
.142
.913
1.096
CPS3
-.042
.048
-.128
-.868
.390
-.139
.055
-.010
-.130
-.119
.872
1.147
CPS4
-.113
.042
-.379
-2.699
.010
-.197
-.029
-.357
-.377
-.371
.961
1.041
CPS5
-.028
.038
-.106
-.731
.469
-.105
.049
-.061
-.109
-.100
.903
1.107
a. Dependent Variable: education
Bootstrap for Coefficients
Model
B
Bootstrapa
Bias
Std. Error
Sig. (2-tailed)
95% Confidence Interval
Lower
Upper
1
(Constant)
2.026
.026
.511
.020
.942
3.350
CPS1
-.041
-.008
.048
.353
-.163
.053
CPS2
.044
.008
.047
.431
-.044
.136
CPS3
-.042
-.002
.052
.471
-.141
.072
CPS4
-.113
.005
.044
.020
-.207
-.009
CPS5
-.028
-.010
.040
.627
-.123
.051
a. Unless otherwise noted, bootstrap results are based on 50 bootstrap samples
Collinearity Diagnosticsa
Model
Dimension
Eigenvalue
Condition Index
Variance Proportions
(Constant)
CPS1
CPS2
CPS3
CPS4
CPS5
1
1
5.187
1.000
.00
.00
.01
.00
.01
.01
2
.246
4.591
.00
.18
.04
.19
.17
.09
3
.209
4.984
.00
.02
.06
.34
.00
.41
4
.189
5.244
.00
.25
.14
.02
.48
.01
5
.141
6.056
.00
.09
.73
.01
.09
.37
6
.028
13.638
.99
.45
.02
.44
.25
.11
a. Dependent Variable: education
Residuals Statisticsa
Statistic
Bootstrapb
Bias
Std. Error
95% Confidence Interval
Lower
Upper
Predicted Value
Minimum
.934
Maximum
1.930
Mean
1.300
-.008
.079
1.147
1.493
Std. Deviation
.2384
.0484
.0713
.1591
.4617
N
50
0
0
50
50
Residual
Minimum
-.6058
Maximum
1.6264
Mean
.0000
.0000
.0000
.0000
.0000
Std. Deviation
.5291
-.0506
.0667
.3172
.5879
N
50
0
0
50
50
Std. Predicted Value
Minimum
-1.537
Maximum
2.641
Mean
.000
.000
.000
.000
.000
Std. Deviation
1.000
.000
.000
1.000
1.000
N
50
0
0
50
50
Std. Residual
Minimum
-1.085
Maximum
2.913
Mean
.000
.000
.000
.000
.000
Std. Deviation
.948
.000
.000
.948
.948
N
50
0
0
50
50
a. Dependent Variable: education
b. Unless otherwise noted, bootstrap results are based on 50 bootstrap samples
Correlations
education
age
education
Pearson Correlation
1
.557**
Sig. (2-tailed)
.000
N
50
50
Bootstrapc
Bias
0
-.006
Std. Error
0
.144
95% Confidence Interval
Lower
1
.259
Upper
1
.803
age
Pearson Correlation
.557**
1
Sig. (2-tailed)
.000
N
50
50
Bootstrapc
Bias
-.006
0
Std. Error
.144
0
95% Confidence Interval
Lower
.259
1
Upper
.803
1
**. Correlation is significant at the 0.01 level (2-tailed).
c. Unless otherwise noted, bootstrap results are based on 50 bootstrap samples
Explanation / Answer
Interpret each table and the conclusion of all the table ony by one .
Correlations
Ho : There is no statistically significant correlation between age and education.
H1 : There is a statistically significant correlation between age and education.
In correlation table gives the correlation test is significant or not .
Correlation between education and age is 0.557
Also give p-value is 0.000 is less than 0.01 so correlation is significant means there is correlation between age and education .
Next table is Bootstrap Specificification
It gives only which sampling method is used so in this problem we use simple random sampling mehod
Also number of sample is 50 and confidence interval is 95%.
Regression
Descriptive statistics
In this table is descriptive statistics like mean , SD and Sample size of each variable also gives confidence interval for each variable.
Basically mean means avarage value of this variable .
SD means variation from the mean of the data (spred of the data )
and sample size give how many obsevation in the data.
Model Summaryb Table
In this table is very important in regression is model good or bad decide on this table using R-Square
Also it give Correlation and standerd error.
Rsuare =0.169
Interpritation - In this model R-square is very low so the proposed model explain 16% variation in the data .
that's we say that the model is not very good .
Bootstrap for model Summary
In this table give Durbin -Watson test is used to detection Autocorrelation
Cirteria - If Durbin -watson test statistics less than 1.0 then there is a autocorrelation .but our problem test statistics is 1.681 is greater than one so there is no autocorrelation .
ANOVAa Table
This table give analysis of variance and is used to checking model significant or not
so in our case Model is notr significant because p-value is greter than 0.05.
Coeficientsa
this table give actual coefficient of model
so the model is
education =2.026-0.041*CPS1+0.044*CPS2-0.042*CPS3-0.113*CPS4-0.028*CPS5
THIS IS THE FINAL MODEL OR FINAL REGRESSION EQUATION .
Bootstrap coefificient is also give coeificient of final regression equation .
Collinearity Diagnosticsa
This is basically used for chacking there is multicolinerity of the regressor or not
using Eigen value and condition indices we conclued the multicolinearity.
If the presence of small eigenvalues indicates collinearity.
If the condition number’ is larger than 1000, the variables are collinear
But Our problem there is no Collinearity
The last table give residual analysis.
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
Best of Luck :)
Correlations
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