Predictive Modeling
Correlation & Multivariate Regression
Uncover relationships between variables and isolate predictive signals.
Recipe Overview & Methodology
Compute pairwise Pearson, Spearman, and Kendall rank correlations. Build automated ordinary least squares (OLS) regression models to understand which factors most heavily influence target KPIs.
Statistical Validation Rules
- Multicollinearity detection using Variance Inflation Factor (VIF).
- Ordinary Least Squares (OLS) model estimation with standard errors.
- Adjusted R-squared assessment for model fit penalty.
- Residual distribution normality checks (Q-Q plot verification).
Sample Output Metrics
R² (Adjusted)
0.784
High explained variance
F-Statistic
142.3
p < 0.0001
Top Predictor
Lead Time (β = -0.42)
Significant coefficient
Residual Std. Error
1.24
Normal error bounds
Execution LogicClustey Engine
# Clustey Automated Regression Engine
import statsmodels.api as sm
X = dataset[['feature_1', 'feature_2', 'feature_3']]
X = sm.add_constant(X)
y = dataset['target_kpi']
model = sm.OLS(y, X).fit()
summary = model.summary()Ready to apply this recipe to your own dataset?
Connect your dataset and generate this analysis in one click.