Title Linear Regression Analysis & Predictive

Title

Linear Regression Analysis & Predictive Modeling (SPSS)

Overview

Executed an end-to-end linear regression analysis in SPSS to evaluate relationships between key financial/operational metrics, assess statistical significance, and build predictive models for decision-making.

Key Analysis & Process

Model Fitting & Diagnostics: Evaluated the overall goodness-of-fit ($R$, $R^2$, and Adjusted $R^2$) to quantify how effectively predictor variables account for variance in the target metric.

ANOVA & Significance Testing: Verified model validity using ANOVA testing ($F$-statistic, $p < 0.05$) to ensure observed linear relationships were statistically significant rather than due to random chance.

Coefficient Evaluation: Analyzed unstandardized and standardized ($\beta$) regression coefficients alongside $t$-statistics and $p$-values to measure the directional impact and individual predictive weight of each independent variable.

Assumption Validation: Examined residual distributions and correlation outputs to ensure linear regression assumptions (linearity, normality of residuals, and homoscedasticity) were fully satisfied.

Outcome

Delivered a validated predictive model and actionable statistical outputs, enabling stakeholders to forecast future outcomes based on key data-driven metrics.