Multiple Linear Regression

Regresyon · Sürekli

🆕 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).

Multiple Linear Regression 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?

Multiple 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 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 Multiple Linear Regression.

Panel assignments (form fields in the program):

Target Column: ziyaretci_haftalik
Predictors: ['alan_m2', 'yesil_alan_orani', 'erisim_skoru', 'bakim_skoru']

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/39_multiple_regression_visitor.xlsx

🎬 Scenario

We predict weekly visitors from several design/management variables; multiple linear regression explains a continuous outcome with several predictors.

⚙️ Variable Selection

Target: visitor_weekly
Predictors: area_m2, green_area_ratio, access_score, maintenance_score
>> ADVANCED PARAMETERS (optional in the form — what they do):
Robust standard errors (HC0-HC3): heteroscedasticity-robust SE; HC3 recommended for small n.
Standardized (beta) coefficients to compare relative effect.
(Diagnostics VIF, Durbin-Watson, Breusch-Pagan, residual Shapiro are already reported.)

Data Preview (First 5 Rows)

park_id	area_m2	green_area_ratio	access_score	maintenance_score	visitor_weekly
1.0	5310.0	0.602	5.0	2.0	678.0
2.0	6269.0	0.422	3.0	4.0	593.0
3.0	5981.0	0.724	2.0	1.0	599.0
4.0	2830.0	0.579	3.0	4.0	424.0
5.0	4398.0	0.95	2.0	2.0	562.0

n = 200 · Columns: park_id, area_m2, green_area_ratio, access_score, maintenance_score, visitor_weekly

📈 MerQur Output

MULTIPLE LINEAR REGRESSION RESULT ───────────────────────────────────────────── R2 = 0.962 Adjusted R2 = 0.961 Weekly visitors ~ area + green ratio + access score + maintenance score

💬 Interpretation

We built a multiple linear regression predicting weekly visitors from four variables — area, green ratio, access score, maintenance score. The model is extremely strong: together they explain 96% of visitor variance (R2 = 0.96). This is a strikingly high explanatory rate — park use is largely determined by these measurable design/ management variables. Such a model shows which factor attracts the most visitors and lets us test “what happens to visitors if we improve access/maintenance by so much” scenarios. Multiple regression is the basic tool for explaining park performance (use) with design and management drivers. >> ADVANCED PARAMETERS (optional in the form — what they do): – Robust standard errors (HC0-HC3): heteroscedasticity-robust SE; HC3 recommended for small n. – Standardized (beta) coefficients to compare relative effect. – (Diagnostics VIF, Durbin-Watson, Breusch-Pagan, residual Shapiro are already reported.)

⚠ 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.

▶ Multiple Linear Regression — video walkthrough

This section is part of the Regresyon video (12 analyses in one video). The link below jumps straight to 0:00, where this analysis begins. Narration is in Turkish.

▶ Watch this analysis (0:00) 📺 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.