CompanyMultiple Regression
H0: There is no significant difference between gender and type of services provided (Tahap Pendidikan)
H1: There is a significant difference between gender and type of services provided (Tahap Pendidikan)
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SUMMARY OUTPUT |
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Regression Statistics |
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|
Multiple R |
0.048392 |
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|
R Square |
0.002342 |
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Adjusted R Square |
-0.02324 |
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Standard Error |
0.90338 |
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Observations |
41 |
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ANOVA |
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|
df |
SS |
MS |
F |
Significance F |
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|
Regression |
1 |
0.074708 |
0.074708 |
0.091543 |
0.763832 |
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Residual |
39 |
31.82773 |
0.816096 |
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Total |
40 |
31.90244 |
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|
|
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Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
Lower 95.0% |
Upper 95.0% |
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Intercept |
3.084034 |
0.461082 |
6.688692 |
5.75E-08 |
2.151408 |
4.016659 |
2.151408 |
4.016659 |
|
Gender |
-0.11345 |
0.374951 |
-0.30256 |
0.763832 |
-0.87185 |
0.644964 |
-0.87185 |
0.644964 |
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Regression is one of the most important methods that is used to identify whether there is a significant relationship between variables in case there is a dependent relationship between variables. It can be seen from the table given above that the value of the level of significance is 0.76>0.05, which means that there is no significant difference between variables. This means that with a change in the independent variable, no significant change will come in the dependent variable. In the present case, an independent variable is gender, and the type of services provided is dependent. Thus, it can be said that the gender factor does not play any role in the sort of services that one will provide to the people in the market. It can be said that males and females both are equally providing services to the people in the market and with a gender change, the type of services provided does not change significantly.
H0: There is not a significant impact of gender on the level of education an individual receives.
H1: There is a significant impact of gender on the level of education an individual receives.
|
SUMMARY OUTPUT |
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|
Regression Statistics |
||||||||
|
Multiple R |
0.278198 |
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|
R Square |
0.077394 |
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|
Adjusted R Square |
0.053737 |
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|
Standard Error |
0.929073 |
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|
Observations |
41 |
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|
ANOVA |
||||||||
|
|
df |
SS |
MS |
F |
Significance F |
|||
|
Regression |
1 |
2.823939 |
2.823939 |
3.271568 |
0.078205 |
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|
Residual |
39 |
33.66387 |
0.863176 |
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Total |
40 |
36.4878 |
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|
|
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|
|
Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
Lower 95.0% |
Upper 95.0% |
|
Intercept |
1.890756 |
0.474195 |
3.987296 |
0.000284 |
0.931606 |
2.849906 |
0.931606 |
2.849906 |
|
Gender |
0.697479 |
0.385614 |
1.808748 |
0.078205 |
-0.0825 |
1.477458 |
-0.0825 |
1.477458 |
Interpretation
The value of the level of significance is 0.07>0.05, which means that the independent variable has a significant impact on the dependent variable. In the present case, the independent variable is the gender factor, and the level of education is the dependent variable. Statistics reflect that with a change in gender factors, the level of education people receive does not change significantly. Hence, males and females receive the same education.
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