Assignment 1: Testing for Multiple Regression
Institutional Affiliation:
Student’s Name:
Date:
Research question
Do education category, gender, and employment status have a significant impact on internet usage?
Hypothesis
Null hypothesis: Education category, gender, and employment status have no significant impact on internet usage.
Alternative hypothesis:Education category, gender, and employment status have a significant impact on internet usage
Results
To determine whether education category, gender, and employment status have a significant effect on internet usage, a multiple linear regression was used. The dependent variable was internet usage while independent variables were; education category, gender, and employment status.
From table 1, R-square =0.226 means that 22.6% of the changes in the internet usage is explained by education category, gender and employment status of the respondent. The value of R-square is below 50% hence the model is not best fitted(Freedman, 2005). Table 2, illustrates the overall relationship between dependent and independent variables. From ANOVA table F=4875.148 corresponding to p-value=0.000 which is less than 0.05 .Hence we reject the null hypothesis and conclude that education category, gender, and employment status have a significant impact on internet usage. Table 3 illustrates the impact of each independent variable on internet usage. There is a significant negative relationship between gender and internet usage (p-value=0.000).The findings indicate that females use less internet than male students. An increase in the education by one level leads to increase in internet usage by 0.278units.Educational level is a significant factor in explaining internet usage( p-value=0.000<0.05).Employed persons use the internet more than unemployed individuals. A change from unemployed to employed increases person's internet usage by 0.066 units. Employment status is a significant factor in explaining internet usage (p-value=0.000 less than 0.05).Social implication is that education level, gender, and employment status affects internet usage of the respondents.
Table 1:
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
|---|---|---|---|---|
| 1 | .475a | .226 | .226 | .51116 |
Table 2
| Sum of Squares | df | Mean Square | F | Sig. | ||
| 1 | Regression | 3821.411 | 3 | 1273.804 | 4875.148 | .000b |
|---|---|---|---|---|---|---|
| Residual | 13110.766 | 50178 | .261 | |||
| Total | 16932.177 | 50181 | ||||
Table 3:
| Standardized Coefficients | t | Sig. | ||||
| B | Std. Error | Beta | ||||
| 1 | (Constant) | .913 | .009 | 106.969 | .000 | |
|---|---|---|---|---|---|---|
| Q101. Gender of respondent | -.037 | .005 | -.032 | -8.052 | .000 | |
| Education Category | .278 | .002 | .455 | 112.240 | .000 | |
| Employment Status | .066 | .005 | .053 | 13.111 | .000 | |
REFERENCE
David A. Freedman (2005). Statistical Models: Theory and Practice, Cambridge University Press.
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