Bayesian Correlation
v1.0.2
Architecture, Planning & Design context — reports the relationship between two continuous variables with BF₁₀. Is the bina_yasi_yil ↔ bakim_maliyeti_TL_m2 relationship real or chance? Supported by Pearson/Spearman/Kendall.
🎯 What is it for?
It reports BF₁₀ instead of the classic correlation p-value; it is especially valuable in small samples and in replication studies. In the Architecture, Planning & Design context, the predicted direction of the relationship (ρ ≈ 0.62) between bina_yasi_yil and bakim_maliyeti_TL_m2 is tested with a Bayes Factor.
📌 When is it used?
- Two continuous variables — to seek direct evidence about direction and strength
- When you want to directly test the “no correlation” hypothesis
- As a replacement for a test that failed with p < .05 at small N
⚙ Assumptions
- Two continuous variables (bina_yasi_yil, bakim_maliyeti_TL_m2).
- A linear relationship (Pearson) or a monotonic one (Spearman/Kendall).
- Outlier screening should be performed beforehand with a Bland-Altman/scatter plot.
📊 How Is It Run in MerQur?
Panel assignments (form fields in the program):
- Columns:
{'x_col': 'yesil_alan_orani', 'y_col': 'memnuniyet'} - Parameters:
{'method': 'pearson'}
📊 Sample Dataset — Architecture, Planning & Design
ℹ Note: The scenario, MerQur output and interpretation below were produced by actually running the real example dataset in MerQur. Numeric results on your own data will differ; the goal is to show how the analysis is set up and interpreted end-to-end.
🎬 Example File
This analysis is demonstrated on the following example dataset for Architecture, Planning & Design:
Peyzaj_Mimarligi/102_bayesian_correlation_green_satisfaction_BF10.xlsx
🎬 Scenario
We assess the strength and evidence factor (BF10) of the green-area-ratio – satisfaction
relationship with Bayesian correlation.
⚙️ Variable Selection
- Variable 1: green_area_ratio | Variable 2: satisfaction
Data Preview (First 5 Rows)
| park_id | green_area_ratio | satisfaction | area_m2 |
|---|---|---|---|
| 1.0 | 0.387 | 3.46 | 29354.0 |
| 2.0 | 0.647 | 4.24 | 25191.0 |
| 3.0 | 0.515 | 3.45 | 42474.0 |
| 4.0 | 0.802 | 3.87 | 26804.0 |
| 5.0 | 0.685 | 3.38 | 26091.0 |
n = 100 · Columns: park_id, green_area_ratio, satisfaction, area_m2
📈 MerQur Output
─────────────────────────────────────────────
r = 0.823 (very strong) BF10 = 4.47e+22 (decisive evidence)
Green-area ratio – satisfaction relationship
💬 Interpretation
We assessed the relationship between green-area ratio and park satisfaction with Bayesian correlation: r = 0.82 is
very strong and the Bayes Factor BF10 = 4.47e+22 makes the evidence for the relationship’s existence decisive. As
green ratio rises, satisfaction rises markedly — quantified evidence of landscape architecture’s core principle
(green = satisfaction/well-being). Unlike classic correlation, Bayesian correlation expresses the strength of the
relationship with a probability distribution and an evidence factor — also answering “how sure are we”. Such a
large BF10 says the probability that the relationship is coincidental is vanishingly small. It is a powerful
framework for quantifying green space’s effect on user satisfaction.
⚠ Common Mistakes
- Pearson is misleading for non-linear relationships — check the scatter plot, and use Spearman if needed.
- Outliers inflate or mask the correlation.
- Correlation ≠ causation (especially in observational studies).
📚 MerQur’a Atıf
Örücü, Ö. K. (2026). MerQur: Integrated Academic Data Analysis & Reporting Platform [Computer software] (Version 1.0.0). https://doi.org/10.53463/merqur.2026001
📝 Üretim Notu — Bu sayfadaki örnek veri sentetik olarak üretilmiştir (sabit SEED=42, generator: samples/Ileri_Duzey_v102/_generate_v102_datasets.py). Sayfa içeriği Anthropic Claude desteği ile hazırlanmış, akademik doğruluk yazar tarafından kontrol edilmiştir.