Example 1
1
Results
Ho: No relationship exists on the Gender * Education level; they are independent.
H1: A relationship exists on the Gender * Education level; they are dependent
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Gender * Education_level 395 100.0% 0 0.0% 395 100.0%
Gender * Education_level Crosstabulation
Education_level
High School Bachelors Masters Ph.D Total
Gender Male Count 40 44 53 57 194
Expected Count 49.1 48.1 48.6 48.1 194.0
Female Count 60 54 46 41 201
Expected Count 50.9 49.9 50.4 49.9 201.0
Total Count 100 98 99 98 395
Expected Count 100.0 98.0 99.0 98.0 395.0
2
Chi-Square Tests
Value df Asymptotic Significance (2-sided)
Pearson Chi-Square 8.006a 3 .046
Likelihood Ratio 8.045 3 .045
Linear-by-Linear Association 7.867 1 .005
N of Valid Cases 395
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 48.13.
Symmetric Measures
Value Asymptotic Standard Errora Approximate Tb Approximate Significance
Interval by Interval Pearson's R -.141- .050 -2.830- .005c
Ordinal by Ordinal Spearman Correlation -.141- .050 -2.829- .005c
N of Valid Cases 395
a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
c. Based on normal approximation.
The p-value is 0.046 which is less than ∝ = 0.05 thus we will reject the null hypothesis.
The gender and education level dependent at 5% level of significance, a statistically significant difference does occur.
3
Example 2
4
Results
Ho: There is no relationship between Age and Glucose level
H1: There is a relationship between Age and Glucose level
Correlations
d Glucose Level
Pearson Correlation 1 .530
Age Sig. (2-tailed) .280
N 6 6
Pearson Correlation .530 1
Glucose_Level Sig. (2-tailed) .280
N 6 6
Pearson value is 0.531 which is positive direct, moderate relationship.
P-value=0.280 which is higher than ∝ = 0.05 thus there is no statistically significant difference between Age and
Glucose level and we fail to reject null hypothesis.
There is a no relationship between Age and Glucose level.
5
Example 3
6
Results
Descriptive Statistics
Mean Std. Deviation N
Distance 423.33 81.720 30
Age 51.00 21.776 30
Correlations
Distance Age
Pearson Correlation Distance 1.000 -.801-
Age -.801- 1.000
Sig. (1-tailed) Distance . .000
Age .000 .
N Distance 30 30
Age 30 30
Variables Entered/Removeda
Variables
Model Variables Entered Removed Method
1 Ageb . Enter
a. Dependent Variable: Distance
b. All requested variables entered.
7
Model Summaryb
Adjusted R Std. Error of the Change Statistics
Model R R Square Square Estimate R Square Change F Change df1 df2 Sig. F Change
1 .801a .642 .629 49.762 .642 50.211 1 28 .000
a. Predictors: (Constant), Age
b. Dependent Variable: Distance
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 124332.643 1 124332.643 50.211 .000b
Residual 69334.024 28 2476.215
Total 193666.667 29
a. Dependent Variable: Distance
b. Predictors: (Constant), Age
Coefficientsa
Standardized
Unstandardized Coefficients Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) 576.682 23.471 24.570 .000
Age -3.007- .424 -.801- -7.086- .000
a. Dependent Variable: Distance
8
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 330.12 522.56 423.33 65.478 30
Std. Predicted Value -1.424- 1.515 .000 1.000 30
Standard Error of Predicted 9.125 16.692 12.663 2.211 30
Value
Adjusted Predicted Value 326.68 524.15 423.05 65.518 30
Residual -78.231- 108.831 .000 48.896 30
Std. Residual -1.572- 2.187 .000 .983 30
Stud. Residual -1.613- 2.274 .003 1.018 30
Deleted Residual -82.861- 117.683 .278 52.481 30
Stud. Deleted Residual -1.663- 2.473 .006 1.045 30
Mahal. Distance .008 2.296 .967 .656 30
Cook's Distance .000 .210 .037 .048 30
Centered Leverage Value .000 .079 .033 .023 30
a. Dependent Variable: Distance
The p-value = 0.0001 and less than ∝ = 𝟎. 𝟎𝟓 so we will reject the null hypothesis.
We can predict the distance from the age as there is statistically significant difference.
The equation is
Distance = 576.682 - -3.007 * Age.
