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Correlations

The document contains a correlation analysis and regression analysis with the variables average, loss, age, gender, course, school, and year level. It finds a significant negative correlation between average and loss, and a significant positive correlation between average and year level. A regression model with these variables was able to predict 19% of the variance in average scores.

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Lala Alal
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84 views3 pages

Correlations

The document contains a correlation analysis and regression analysis with the variables average, loss, age, gender, course, school, and year level. It finds a significant negative correlation between average and loss, and a significant positive correlation between average and year level. A regression model with these variables was able to predict 19% of the variance in average scores.

Uploaded by

Lala Alal
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
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Correlations

average loss
average Pearson Correlation 1 -.201
Sig. (2-tailed) .161
N 50 50
loss Pearson Correlation -.201 1
Sig. (2-tailed) .161
N 50 50

Correlations
average loss Age Gender Course School YearLevel
average Pearson Correlation 1 -.201 -.026 -.207 .236 .a .344*
Sig. (2-tailed) .161 .857 .149 .099 . .014
N 50 50 50 50 50 50 50
loss Pearson Correlation -.201 1 .219 -.075 -.174 .a -.089
Sig. (2-tailed) .161 .126 .605 .228 . .537
N 50 50 50 50 50 50 50
Age Pearson Correlation -.026 .219 1 -.278 -.170 .a -.203
Sig. (2-tailed) .857 .126 .051 .237 . .157
N 50 50 50 50 50 50 50
Gender Pearson Correlation -.207 -.075 -.278 1 -.066 .a -.058
Sig. (2-tailed) .149 .605 .051 .648 . .689
N 50 50 50 50 50 50 50
Course Pearson Correlation .236 -.174 -.170 -.066 1 .a .646**
Sig. (2-tailed) .099 .228 .237 .648 . .000
N 50 50 50 50 50 50 50
School Pearson Correlation .a .a .a .a .a .a .a
Sig. (2-tailed) . . . . . .
N 50 50 50 50 50 50 50
YearLevel Pearson Correlation .344* -.089 -.203 -.058 .646** .a 1
Sig. (2-tailed) .014 .537 .157 .689 .000 .
N 50 50 50 50 50 50 50
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
a. Cannot be computed because at least one of the variables is constant.
Variables Entered/Removeda
Variables Variables
Model Entered Removed Method
1 YearLevel, . Enter
Gender, loss,
Age, Courseb
a. Dependent Variable: average
b. All requested variables entered.

Model Summary
Adjusted R Std. Error of the
Model R R Square Square Estimate
1 .436a .190 .098 .58339
a. Predictors: (Constant), YearLevel, Gender, loss, Age, Course

ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 3.505 5 .701 2.060 .089b
Residual 14.975 44 .340
Total 18.480 49
a. Dependent Variable: average
b. Predictors: (Constant), YearLevel, Gender, loss, Age, Course

Coefficientsa
Standardized
Unstandardized Coefficients Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) 2.387 1.182 2.020 .050
loss -.169 .121 -.196 -1.393 .171
Age .010 .054 .027 .180 .858
Gender -.272 .198 -.196 -1.377 .175
Course -.029 .221 -.024 -.133 .895
YearLevel .156 .084 .336 1.871 .068
a. Dependent Variable: average

One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
average 50 2.5200 .61412 .08685
loss 50 2.3200 .71257 .10077

One-Sample Test
Test Value = 3
95% Confidence Interval of the
Difference
t df Sig. (2-tailed) Mean Difference Lower Upper
average -5.527 49 .000 -.48000 -.6545 -.3055
loss -6.748 49 .000 -.68000 -.8825 -.4775

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