Bayesian Linear Regression

Regresyon · Bayesian

Bayesian Linear Regression is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Education Sciences sample dataset — how the analysis is run, what the MerQur output looks like, and how the result is reported in APA 7 format.

🎯 What is it for?

Bayesian Linear Regression automatically performs, in the background, all the assumption checks required for the relevant data type (normality, homogeneity of variance, etc.) and presents the results with a clear table and chart. Automatic interpretation to the APA 7 standard, effect sizes such as Cohen’s d/η²/R², and 95% confidence intervals are reported.

📌 When is it used?

Statistical analysis of measurements in the Education Sciences domain
To produce APA 7-compatible result tables for academic publications
Hypothesis testing and decision-making processes
Undergraduate / master’s / PhD theses after the appropriate method has been selected

📐 Assumptions

Appropriate scale — Variables must be at the measurement level required by the analysis (nominal/ordinal/interval/ratio)
Independent observations — Observations must come from individuals independent of one another
Sufficient sample size — The minimum n requirement for the analysis must be met
Outlier check — Outliers must be detected and evaluated

If assumptions are violated, MerQur automatically suggests a non-parametric or robust alternative.

🛠 How to do it in MerQur

Load the data. Select the sample file from File → Open. MerQur auto-detects column types.

Select the analysis. From the left side select Bayesian Linear Regression.

Panel assignments (form fields in the program):

y_col: test_puan
x_cols: ['ogrenci_id', 'calisma_saat', 'yas']
prior_precision: 0.01

Optional settings. Effect size ✓ · 95% confidence interval ✓ · Assumption checks (automatic).

▶ Run — click the button. Results are produced automatically as a table + chart.

📄 Export to Word. APA 7-formatted report with italic statistical symbols.

📊 Sample Dataset — Education Sciences

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 Education Sciences:

Egitim_Bilimleri/47_bayesian_study_achievement.xlsx

🎬 Scenario

Here we model test performance for 100 students using their weekly study_hour and their age. Rather than relying only on point estimates, we want full probability distributions for the regression coefficients and to incorporate uncertainty directly. We use Bayesian Linear Regression because it yields posterior distributions and credible intervals for how study hours and age predict the test_score, which is a continuous outcome.

⚙️ Variable Selection

Target (DV): test_score
Predictor: study_hour
Predictor: age

Data Preview (First 5 Rows)

student_id	study_hour	age	test_score
1.0	2.2	16.0	60.6
2.0	24.6	20.0	72.4
3.0	4.4	16.0	58.2
4.0	33.6	16.0	73.7
5.0	7.4	17.0	60.7

n = 100 · Columns: student_id, study_hour, age, test_score

📈 MerQur Output

BAYESIAN LINEAR REGRESSION RESULT ───────────────────────────────────────────── Conjugate Normal-Inverse-Gamma prior posterior sigma2 = 23.72 Test score ~ study hours + age

💬 Interpretation

We used Bayesian linear regression to predict test score from study hours and age. The Bayesian advantage: the result is not a single p-value but a probability distribution (posterior) for the parameters — we get a credible interval for each coefficient and a “probability the effect is positive”. The posterior error variance is sigma2 = 23.72. The Bayesian method honestly expresses uncertainty and can incorporate prior knowledge. It is valuable in educational research with small samples or where information must be carried over from previous studies.

⚠ Common Mistakes

Misidentifying the data type (e.g., loading a categorical variable as numeric)
Skipping assumption checks and going straight to the p-value
Failing to report effect size — APA 7 requires both p and effect size
Failing to apply a Type I error correction (Bonferroni/Tukey) in multiple comparisons
Not switching to a non-parametric alternative when n is insufficient

📹 Video Walkthrough

Watch the video below for an end-to-end walkthrough of this analysis on a Education Sciences file.

▶ Bayesian Linear Regression — video walkthrough

This section is part of the İleri Düzey II — Bayesçi, Mekânsal ve Yol Modelleri video (8 analyses in one video). The link below jumps straight to 3:28, where this analysis begins. Narration is in Turkish.

▶ Watch this analysis (3:28) 📺 All videos

🔀 The same analysis in other disciplines:
🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 🏥 Health Sciences · 📊 Social, Humanities & Admin Sciences · 🏃 Sport Sciences · 🌾 Agriculture, Forestry & Aquatic

📚 If You Used This Analysis, Cite MerQur

If you performed this analysis using MerQur in a scientific study, please use the citation below as part of your academic citation obligations (APA 7):

Örücü, Ö. K. (2026). MerQur: Integrated Academic Data Analysis & Reporting Platform [Computer software] (Version 1.0.0). https://doi.org/10.53463/merqur.2026001

For BibTeX, RIS, EndNote and the English citation form: all citation formats →

Sources:

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.