Publication Date
Spring 2026
Course Name
Statistical Techniques for Research Inquiry
Course Number
MATH 8090
Subject
Mathematics
Abstract
This paper applies multiple linear regression analysis to examine whether life outlook and sociability predict happiness. Using a dataset of 38 observations, the study employs IBM SPSS to conduct exploratory data analysis, assess regression assumptions, and evaluate model validity. Key assumptions—including linearity, homoscedasticity, independence of residuals, multicollinearity, normality, and the presence of influential outliers—are systematically examined using statistical tests and visual diagnostics. While some deviations from normality and potential nonlinear patterns are identified, the analysis proceeds based on sample size considerations and overall assumption adequacy. Results indicate that the regression model is not statistically significant and explains only a small proportion of the variance in happiness. Neither life outlook nor sociability is found to be a significant predictor of happiness. The paper concludes with a reflective discussion of the analytical process, emphasizing the importance of assumption testing, visual interpretation, and cautious inference in regression analysis. [Abstract generated by AI.]
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Loyd, Sam, "Multiple Variable Regression Assignment" (2026). Distinguished Student Scholarship Collection. 15.
https://fuse.franklin.edu/dssc/15
