MAT 125 Regression Analysis Assignment: Housing Price Forecast
MAT 125 Regression Analysis Assignment Overview
This MAT 125 regression analysis assignment showcases how our statistics experts build data-driven forecasts that earn top marks. The project analyzes a 250-record housing dataset to predict median sale price using square footage, bedrooms, age, lot size, and school rating. We completed the entire regression analysis homework—from exploratory data analysis to final recommendation deck—with clear explanations tailored to an undergraduate statistics rubric.
Course: MAT 125 - Regression Analysis & Forecasting | Author: Sofia Martinez | Project: Housing Price Prediction Using Multiple Regression | Date: November 12, 2025
Key Learning Outcomes
- Exploratory Data Analysis: Visualized relationships and detected multicollinearity using correlation matrices and scatterplots.
- Multiple Regression Modeling: Built and compared baseline, stepwise, and interaction models to maximize adjusted R2.
- Diagnostic Testing: Evaluated normality, homoscedasticity, independence, and leverage with residual plots and statistical tests.
- Predictive Accuracy: Generated prediction intervals and cross-validated performance with holdout samples.
- Managerial Interpretation: Translated coefficients into actionable insights for real estate investors.
Executive Summary
The final MAT 125 regression analysis assignment delivers a market-ready forecasting model explaining 86.4% of price variance (adjusted R2=0.864). After cleaning the dataset, we retained four significant predictors—square footage, lot size, age, and school rating—and introduced an interaction between square footage and school rating that improved RMSE by 12%. Recommendations are supported with visuals, statistical evidence, and sensitivity analysis for investors evaluating property upgrades.
Dataset & Variable Preparation
The assignment began with importing a CSV dataset into Excel and SPSS. We validated data types, winsorized top 1% outliers in price and square footage, and standardized continuous predictors for interpretability. Categorical features (neighborhood tier) were encoded using dummy variables. Missing school rating values (<3% of records) were imputed using regression on median household income.
- Dependent Variable: Median housing sale price (USD).
- Independent Variables: Square footage, bedrooms, bathrooms, property age, lot size, walkability index, school rating, neighborhood tier.
- Software: Microsoft Excel (Data Analysis ToolPak) & IBM SPSS v29.
Model Development Workflow
We tested three modeling strategies required by the MAT 125 rubric. The final specification was selected using adjusted R2, AIC, and domain logic.
- Baseline OLS Model: Included all predictors, yielding signs of multicollinearity (VIF > 10 for bedrooms and square footage).
- Stepwise Selection: Removed redundant variables; introduced interaction term SF*SCHOOL to capture premium effect.
- Validation: 80/20 train-test split with comparably high R2 (0.872 train vs 0.853 test) confirming model stability.
Diagnostic Testing & Assumptions
Diagnostics satisfied MAT 125 requirements for regression credibility:
- Normality: Shapiro-Wilk p-value 0.214; Q-Q plot points aligned with reference line.
- Homoscedasticity: Breusch-Pagan test p-value 0.318; residual vs fitted plot showed random dispersion.
- Independence: Durbin-Watson statistic 1.97 indicating minimal autocorrelation.
- Influential Points: Cook's distance < 0.45 for all observations after removing two leverage outliers.
Interpretation of Coefficients
The narrative portion explains what each statistically significant coefficient means for decision-makers. For example, every additional 100 square-foot increase in homes within high-rated school districts adds $18,460 to predicted sale price (p < 0.01). Aging properties reduce value by $1,120 per year (p < 0.05) unless offset by lot size improvements. A sensitivity table shows expected price ranges under different renovation budgets.
Presentation Deliverables
The complete MAT 125 regression homework submission includes:
- 10-page APA-formatted report with methodology, tables, and interpretation.
- Excel workbook containing cleaned dataset, pivot tables, and automated charts.
- SPSS output file (.spv) with ANOVA table, coefficients, residual analysis, and diagnostics.
- Executive summary slide deck with recommendations and scenario analysis.
Sample Output Snapshot
Regression Statistics
Multiple R: 0.932
R Square: 0.868
Adjusted R Square: 0.864
Standard Error: 21435.27
Observations: 250
ANOVA
F(6,243) = 258.41, p < 0.001
Results & Grade
Final Grade: A+ (99/100)
Feedback highlighted the professional-level data storytelling, clear regression diagnostics, and actionable recommendations for pricing strategy. The professor noted that the submission exceeded expectations for MAT 125 by providing full reproducibility and executive-ready visuals.
Why This Regression Assignment Stood Out
- Comprehensive adherence to MAT 125 regression analysis rubric and APA formatting.
- Robust diagnostics confirming the validity of regression assumptions.
- Managerial insights paired with statistical evidence for non-technical stakeholders.
- Replicable Excel/SPSS workflow enabling instructor verification.
- Polished visualization pack explaining trends, residuals, and forecast ranges.
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