Business Statistics: Regression Analysis and Interpretation of Results

This assignment solution focuses on regression analysis within a business statistics context. The document presents two multiple regression analyses. The first explores the relationship between gender and the type of services provided, concluding that gender does not significantly influence service types, with a significance level of 0.76. The second analysis investigates the impact of gender on the level of education received, indicating a significant impact of gender on education levels, with a significance level of 0.07. The solution includes detailed interpretations of the regression outputs, including statistical significance, coefficients, and p-values, providing insights into the relationships between the variables and the interpretation of the results.

BUSINESS STATISTICS

TABLE OF CONTENTS
Multiple regression..........................................................................................................................1

Multiple regression
H0: There is no significant difference between gender and type of services provided (Tahap
Pendidikan)
H1: There is significant difference between gender and type of services provided (Tahap
Pendidikan)
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.048392
R Square 0.002342
Adjusted R
Square -0.02324
Standard
Error 0.90338
Observations 41
ANOVA
df SS MS F
Significance
F
Regression 1 0.074708 0.074708 0.091543 0.763832
Residual 39 31.82773 0.816096
Total 40 31.90244
Coefficients
Standard
Error t Stat P-value Lower 95%
Upper
95%
Lower
95.0%
Upper
95.0%
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
Interpretation
Regression is the one of the most important method that is used to identify whether there
is significant relationship between variables in case there is dependent relationship between
variables. It can be seen from the table given above that value of level of significance is
0.76>0.05 which means that there is no significant difference between variables. Means that with
change in independent variable no significant change will come in the dependent variable. In
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present case independent variable is gender and type of services provided is dependent. Thus, it
can be said that 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 male and female both are equally providing
services to the people in the market and with change in gender type of services provided does not
change significantly.
H0: There is not significant impact of gender on the level of education individual received.
H1: There is significant impact of gender on the level of education individual received.
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.278198
R Square 0.077394
Adjusted R
Square 0.053737
Standard
Error 0.929073
Observations 41
ANOVA
df SS MS F
Significance
F
Regression 1 2.823939 2.823939 3.271568 0.078205
Residual 39 33.66387 0.863176
Total 40 36.4878
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
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Interpretation
Value of level of significance is 0.07>0.05 which means that independent variable has
significant impact on the dependent variable. In present case independent variable is gender
factor and level of education is dependent variable. Statistics reflect that with change in gender
factor level of education people received does not change significantly. Hence, male and female
receive same education.
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