STL Decomposition

Zaman Serisi · Mevsimsellik

🆕 New in v1.0.5: A Chart tab was added to this analysis (boxplot, interaction, profile, bar, scatter, biplot, or forecast — depending on analysis type).

STL Decomposition is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Architecture, Planning & Design 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?

STL Decomposition automatically performs all required assumption checks (normality, homogeneity of variance, etc.) for the relevant data type in the background and presents the results with a clear table + 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 Architecture, Planning & Design 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 STL Decomposition.

Panel assignments (form fields in the program):

Tarih sutunu: tarih
Value Column: PM25_ugm3
Period: 7
Robust: False

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 — 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/95_stl_PM25_daily.xlsx

🎬 Scenario

We decompose the daily PM2.5 series into trend + seasonal + residual components; STL is appropriate.

⚙️ Variable Selection

Date: date | Value: PM25_ugm3

Data Preview (First 5 Rows)

date	PM25_ugm3
2022-01-01 00:00:00	21.6
2022-01-02 00:00:00	21.2
2022-01-03 00:00:00	24.7
2022-01-04 00:00:00	28.4
2022-01-05 00:00:00	17.9

n = 1095 · Columns: date, PM25_ugm3

📈 MerQur Output

STL DECOMPOSITION RESULT ───────────────────────────────────────────── Period (seasonal length) = 7 (daily -> weekly cycle) Series decomposed into trend + seasonal + residual components

💬 Interpretation

STL (Seasonal-Trend decomposition using Loess) splits a daily PM2.5 (air pollution) series into three components: long-term trend, a recurring cycle (7-day = weekly) and the remaining residual. The weekly period is meaningful — air pollution fluctuates regularly across the week (traffic/activity pattern). This decomposition clarifies “is pollution generally rising, or just fluctuating weekly”. STL is the most intuitive way to interpret seasonal/ cyclical environmental series (pollution, temperature), separating trend from cycle to see green spaces’ long-term effect on air quality.

⚠ 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 Architecture, Planning & Design file.

▶ STL Decomposition — video walkthrough

This section is part of the Zaman Serisi Analizi video (6 analyses in one video). The link below jumps straight to 1:28, where this analysis begins. Narration is in Turkish.

▶ Watch this analysis (1:28) 📺 All videos

🔀 The same analysis in other disciplines:
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · ⚙ 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.