X-Weight Pro Calibration

Enterprise-grade sample weighting with our patented hybrid algorithm

Input Parameters

1. Upload Sample Data

Drag & drop your .DAT file here or click to browse

CSV, SPSS, or X-Weight DAT format
2. Demographic Constraints

Upload your .RES constraints file or use presets

3. Calibration Method
Our hybrid method combines the best of linear and multiplicative approaches
4. Advanced Settings

Performance Metrics

0.42s
Processing Time
3.2x faster than industry average
94%
Effective Sample
Industry avg: 82%
0.08
Weight Stability (σ)
Lower is better
5
Convergence Steps
Typical: 3-5 steps

Initial vs. Final Weights Dual Axis

Weight Change Distribution Δ Analysis

Weight Efficiency Metrics

Adjustment Factor Distribution

Factor Correlation Analysis

Demographic Alignment

Algorithm Convergence

Iteration History

1
Initial Setup
Parameters validated, initial weights calculated
2
Linear Phase
Primary constraints applied (R² = 0.89)
3
Multiplicative Adjustment
Fine-tuning weights (Δ = 0.42)
4
Boundary Enforcement
Clipping outliers (3.2% of weights adjusted)
5
Final Optimization
Variance minimization (σ reduced by 28%)

Methodology Comparison Benchmark

Effective Sample Size

Convergence Speed

Method SEFF Time Stability Constraints Outliers Rating
X-Weight Hybrid 94% 0.42s 0.08 100% 3.2% Best
Linear Regression 82% 0.38s 0.14 92% 8.7% Good
Multiplicative Raking 79% 1.24s 0.21 88% 12.3% Fair
Entropy Balancing 85% 2.15s 0.12 95% 6.5% Good
Post-Stratification 76% 0.56s 0.18 83% 15.1% Limited

Robustness Analysis Monte Carlo

Stability Scores

Error Distribution Analysis

Residual Diagnostics

Diagnostic Report

Calibration successful - All quality indicators within optimal ranges
Weight Distribution
  • No negative weights
  • 95% within bounds
  • Gini coefficient: 0.18
Demographic Fit
  • All targets matched
  • Max Z-score: 1.8
  • Average error: 0.2%
Statistical Quality
  • SEFF: 94%
  • Design effect: 1.06
  • Reliability: 98/100