PLS Regression
PLS Regression is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Agriculture, Forestry & Aquatic 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?
PLS Regression automatically applies 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 in APA 7 format, 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 Agriculture, Forestry & Aquatic 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 PLS Regression.
Panel assignments (form fields in the program):
- y_col:
yaprak_id - x_cols:
[wave_1, wave_2, wave_3, +9 daha] - n_components:
2
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 — Agriculture, Forestry & Aquatic
ℹ Note: The MerQur output and interpretation tables on this page are illustrative, intended to help readers understand the analysis. Numeric results from your own data may differ; this page does not need to match the YouTube video walkthrough exactly.
🎬 File Used in the Video
The YouTube video for this page was recorded for the Agriculture, Forestry & Aquatic domain using the sample file below:
MerQur_Hoca_Tanitim/Ziraat_Orman_Su/44_pls_spektral_klorofil.xlsx
Data Preview (First 5 Rows)
| parsel_id | bant_01 | bant_02 | bant_03 | bant_04 | bant_05 | bant_06 | bant_07 | bant_08 | bant_09 | bant_10 | bant_11 | bant_12 | NDVI |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.00 | -0.58 | 0.21 | -0.13 | -0.46 | -0.37 | 0.49 | -0.37 | 0.28 | 0.09 | -0.55 | -0.09 | -0.09 | 0.57 |
| 2.00 | 1.54 | 0.40 | -0.59 | -0.15 | 0.23 | -0.24 | 0.54 | 0.60 | -0.09 | -0.15 | -0.36 | -0.61 | 0.65 |
| 3.00 | 0.05 | -0.98 | 0.90 | 1.17 | 0.56 | 1.99 | -1.35 | -1.14 | 2.76 | 1.39 | 1.17 | -0.57 | 0.39 |
| 4.00 | 1.23 | 0.06 | -1.07 | 0.86 | 0.64 | 0.34 | 0.37 | -0.54 | 0.46 | 0.71 | 1.16 | -1.72 | 0.74 |
| 5.00 | -1.32 | -0.38 | 1.04 | 0.12 | 0.02 | 0.09 | -0.38 | -0.57 | 0.57 | 0.36 | -0.14 | 1.07 | 0.24 |
n = 200 · Columns: parsel_id, bant_01, bant_02, bant_03, bant_04, bant_05, bant_06, bant_07, bant_08, bant_09, bant_10, bant_11, bant_12, NDVI
MerQur Output (Illustrative)
MerQur Narrative Interpretation
The multiple regression model showed that approximately 52% of the variability in NDVI was explained by the predictor variables (R² = .52, p < .001). The model is significant overall and provides a good fit to the data.
APA 7 Interpretation (Academic Format · Illustrative)
NDVI variable, F(3, 196) = 47.62, p < .001, R² = .52, adjusted R² = .51. This result supports that the predictor variables in the model explained approximately 52% of the variance in NDVI.⚠ 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 Agriculture, Forestry & Aquatic file.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 🏥 Health Sciences · 📊 Social, Humanities & Admin Sciences · 🏃 Sport Sciences
📚 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 →
- 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.