Non-linear Regresyon

Regresyon · Doğrusal Olmayan

Non-linear Regression is one of the statistical analyses automatically performed in MerQur. This page provides a representative illustration of how the analysis is applied to a sample dataset from the field of Agriculture, Forestry and Aquaculture, how the MerQur output appears, and how it is reported in APA 7 format.

🎯 What is it for?

Non-linear Regression automatically applies all the assumption checks required for the relevant data type (normality, homogeneity of variance, etc.) in the background and presents the results in 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?

In the statistical analysis of measurements belonging to the Agriculture, Forestry and Aquaculture field
To produce APA 7 compliant result tables for academic publication
In hypothesis testing and decision-making processes
After selecting an appropriate method in undergraduate / master’s / doctoral theses

📐 Assumptions

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

If the assumptions are violated, MerQur automatically steers you toward a non-parametric or robust alternative.

🛠 How to do it in MerQur

Load the data. Select the sample file from the File → Open menu. MerQur automatically detects the column types.

Select the analysis. Choose Non-linear Regression from the left sidebar.

Panel assignments (form fields in the program):

y_col: yas_yil
x_col: deney_id
function_preset: michaelis

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

Press the ▶ Run button. The results are generated automatically as a table + chart.

📄 Export to Word. Report in APA 7 format with italic statistical symbols.

📊 Sample Dataset — Ziraat, Orman ve Su Ürünleri

ℹ Note: The MerQur output and interpretation tables on this page are illustrative and intended to help understand the analysis. The numerical results for your own data may differ; this page does not have to match the YouTube video walkthrough exactly.

🎬 Example File Used in the Video

The YouTube video walkthrough of this page was recorded for the Agriculture, Forestry and Fisheries field using the sample file below:

MerQur_Hoca_Tanitim/Ziraat_Orman_Su/48_nonlinear_gompertz_yas_dbh.xlsx

Topic: Application of Non-linear Regression analysis on a real sample data set from the Agriculture, Forestry and Fisheries field.

Data Preview (First 5 Rows)

agac_id	yas_yil	boy_m
1.00	30.70	27.50
2.00	11.10	12.85
3.00	50.20	31.10
4.00	21.00	21.72
5.00	23.80	23.71

n = 80 · Sütunlar: agac_id, yas_yil, boy_m

MerQur Output (Illustrative)

REGRESSION MODEL ───────────────────────────────────────────── Dependent: boy_m Predictors: agac_id, yas_yil Model: F(3, 76) = 47.62, p < .001 R² = 0.524 Adj R² = 0.512 Coefficients: Constant: 12.43 (SE=2.18, p<.001) X1: 0.418 (SE=0.094, β=.32, p<.001) X2: -0.215 (SE=0.087, β=-.18, p=.014)

MerQur Interpretation (Plain Language)

The multiple regression model showed that approximately 52% of the variability in boy_m is explained by the predictor variables (R² = .52, p < .001). The model is significant overall and fits the data well.

APA 7 Interpretation (Academic Format · Illustrative)

The multiple linear regression analysis showed that the overall model was statistically significant in predicting the boy_m variable, F(3, 76) = 47.62, p < .001, R² = .52, adjusted R² = .51. This result supports that the predictor variables in the model explain approximately 52% of the variance in boy_m.

⚠ Common Mistakes

Specifying the data type incorrectly (e.g., loading a categorical variable as numeric)
Skipping the assumption checks and looking directly at the p-value
Not reporting the effect size — APA 7 requires both p and the effect size
Not applying a Type I error correction (Bonferroni/Tukey) in multiple comparisons
Not switching to a non-parametric alternative when n is insufficient

📹 Video Walkthrough

You can watch the video below for an end-to-end application of this analysis on the Agriculture, Forestry and Fisheries dataset.

▶

The video will soon be uploaded to the MerQur YouTube channel

📺 MerQur YouTube Channel

🔀 The same analysis in other Core Fields:
🎓 Educational Sciences · 🧪 Natural Sciences and Mathematics · 🏛 Architecture, Planning and Design · ⚙ Engineering · 🏥 Health Sciences · 📊 Social, Humanities and Administrative Sciences · 🏃 Sport Sciences

📚 If You Used This Analysis, Cite MerQur

If you performed this analysis in a scientific study using MerQur, please use the following citation (APA 7) in accordance with the academic citation requirement:

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

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

References:

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.