Multiple Linear Regression
Multiple Linear Regression is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Health 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?
Multiple Linear Regression automatically performs all assumption checks required for the data type (normality, homogeneity of variance, etc.) in the background and presents the results in a clear table + chart. Automatic APA 7-formatted interpretation, effect sizes (Cohen’s d, η², R²) and 95% confidence intervals are reported.
📌 When is it used?
- Statistical analysis of measurements in the Health 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 Multiple Linear Regression.
Panel assignments (form fields in the program):
- Target Column:
sistolik_KB - Predictors:
['yas', 'BMI', 'tuz_g_gun', 'egzersiz_saat_hafta']
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 — Health 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 Health Sciences:
Tip/39_multiple_regression_bp.xlsx
🎬 Scenario
We model systolic BP with four predictors (age, BMI, salt, exercise). To explain a continuous outcome with multiple variables, multiple regression is appropriate.
⚙️ Variable Selection
- Dependent variable: systolic_bp
- Predictor(s): age
- Predictor(s): BMI
- Predictor(s): salt_g_day
- Predictor(s): exercise_hour_week
- >> 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)
| patient_id | age | BMI | salt_g_day | exercise_hour_week | systolic_bp |
|---|---|---|---|---|---|
| 1.0 | 78.0 | 27.91 | 11.6 | 5.2 | 195.0 |
| 2.0 | 40.0 | 23.73 | 12.6 | 6.9 | 177.0 |
| 3.0 | 59.0 | 25.99 | 7.5 | 2.8 | 164.0 |
| 4.0 | 50.0 | 31.12 | 12.7 | 1.0 | 176.0 |
| 5.0 | 26.0 | 28.41 | 4.0 | 9.2 | 137.0 |
n = 200 · Columns: patient_id, age, BMI, salt_g_day, exercise_hour_week, systolic_bp
📈 MerQur Output
💬 Interpretation
We modeled systolic blood pressure with four predictors (age, BMI, daily salt, exercise) at once: the model explains 68.3% of variance (Adj. R^2 = 0.68) — strong explanatory power. Multiple regression gives each predictor’s pure contribution to BP with the others held constant; thus it answers “which factor really raises BP?” while controlling confounders. In medicine it is the core method for identifying the risk factors that drive a clinical indicator and for prediction. >> 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 Health Sciences file.
▶ Multiple Linear Regression — video walkthrough
A complete end-to-end walkthrough of this analysis in MerQur, narrated on screen. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 📊 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 →
- 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.