Introduction
In statistical analysis, it is essential to understand how variables are related since this is key to making informed decisions and predictions. Multiple regression analysis, which examines how multiple predictor variables are combined with an outcome variable, has been a powerful tool. This essay aims to examine the extent to which Grade Point Average (GPA) and family size relate to variations in income.
GPA and Family Size’s Predictive Power of Income
Research analysis focused on family size, income, and GPA to determine the extent to which changes in income can be attributed to these variables combined. Multiple regression analysis helps to determine how well GPA and family size together predict income (Didit & Nikmah, 2020). Looking at the regression coefficients can provide clues about the relationships among these variables, including their strength and direction. In this regard, while GPA and family size are independent variables, income is the dependent variable.
The multiple regression results show that we can use the GPA and family size coefficients as indicators of income, respectively. A positive sign indicates a positive relationship, meaning the outcome variable increases as the predictor variable increases. Conversely, a negative sign indicates a negative relationship, implying that an increase in the predictor is associated with a decrease in the outcome variable. In addition, the significance levels of the coefficients indicate the reliability of the relations (He et al., 2021). The identified significant coefficient indicates that the relationship between the predictor and the outcome variable cannot be due to chance alone.
Attribution of Changes
Individual contributions of every predictor variable under the multiple regression model are examined in order to find out the extent to which a change in income can be attributed to family size and GPA. By separating the effects of GPA and family size on income, we may ultimately determine their relative impacts on changes in income. Regression coefficients for GPA and family size indicate how each variable uniquely contributes to shifts in income.
A higher-magnitude coefficient indicates a stronger influence of the predictor variable on income, while a lower value indicates a weaker relationship (Didit & Nikmah, 2020). Furthermore, the R-squared value, which is the proportion of variance explained by each predictor variable, provides valuable information about the importance of GPA and family size in predicting income levels.
By comparing the coefficients and R-squared values for both GPA and Family Size, one can see that the extent of the change in earnings depends on which factor is at work. If the GPA has a higher coefficient value and a greater contribution to variance, then it would indicate that changes in the grade point average have a greater impact on earnings than changes in family size (Kanakriyah, 2020). Otherwise, if family size has a higher coefficient and explains more variation in income, then it means that variations in family size play a consequential role in determining who gets money or not. This multiple regression analysis helps us break down how much GDP affects our country’s economic growth and whether it remains efficient.
Multiple Regression Equation
The multiple regression equation can be constructed using the formula Income = β0 + (β1 * GPA) + (β2 * Family_Size) (Kim & Oh, 2021). In this level, β0 is the intercept, β1 is the coefficient for GPA, and β2 is the coefficient for Family_Size. When performing the analysis, one can see that the intercept is 14878.7244, the Coefficient for GPA is -2415.5236, and the Coefficient for Family_Size is 1228.9403 (See Figure 1). When substituting the data value into the above formula, the result is Income = 14878.7244 + (2415.5236 * GPA) + (1228.9403 * Family_Size).
Conclusion
The essay findings show that various income-related factors can be predicted from GPA and family size. In this way, it is possible to gain insights into the contributions of these variables to the variations observed in their responses to income. The study thus forms part of future analyses in statistics, providing a stepping stone into more complex territory where predictive models exist.
References
Didit, D. D., & Nikmah, N. R. S. (2020). The role of remuneration contribution and social support in organizational life to build work engagement. Journal of Islamic Economics Perspectives, 1(2), 20-32.
He, J., Erfani, S., Ma, X., Bailey, J., Chi, Y., & Hua, X. S. (2021). Alpha-IoU: A family of power intersection over union losses for bounding box regression. Advances in Neural Information Processing Systems, 34, 20230-20242.
Kanakriyah, R. (2020). Model to determine main factors used to measure audit fees. Academy of Accounting and Financial Studies Journal, 24(2), 1-13.
Kim, Y., & Oh, H. (2021). Comparison between multiple regression analysis, polynomial regression analysis, and an artificial neural network for tensile strength prediction of BFRP and GFRP. Materials, 14(17), 1-13.
Appendix
Figure 1. Research Data