Logistic Regression

Regresyon · İkili

🆕 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).

Logistic 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?

Logistic 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 Logistic Regression.

Panel assignments (form fields in the program):

Target Column: kalp_atagi
Predictors: ['yas', 'kolesterol', 'sigara', 'sistolik_KB']

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/40_logistic_KAH.xlsx

🎬 Scenario

We model heart attack (yes/no) with four risk factors. For a binary outcome, logistic regression is appropriate.

⚙️ Variable Selection

Dependent variable: heart_attack
Predictor(s): age
Predictor(s): cholesterol
Predictor(s): smoking
Predictor(s): systolic_bp
>> ADVANCED PARAMETERS (optional in the form — what they do):
Pseudo-R squared: Cox-Snell and Nagelkerke (besides McFadden).
Classification metrics: accuracy / sensitivity / specificity / AUC (cutoff 0.5).
Hosmer-Lemeshow goodness-of-fit test; VIF for predictors.

Data Preview (First 5 Rows)

patient_id	age	cholesterol	systolic_bp	heart_attack
1	62	253	118	0
2	72	224	123	1
3	53	152	145	1
4	40	308	121	0
5	51	178	179	0

n = 300 · Columns: patient_id, age, cholesterol, smoking, systolic_bp, heart_attack

📈 MerQur Output

LOGISTIC REGRESSION RESULT ───────────────────────────────────────────── Pseudo R^2 = 0.095 binary outcome: heart_attack predictors: age, cholesterol, smoking, systolic_bp

💬 Interpretation

We modeled a binary outcome (heart attack yes/no) with four risk factors: the model has weak-to-moderate explanatory power (pseudo R^2 = 0.10) and gives each predictor’s effect on the odds. Logistic regression replaces linear regression when the outcome is binary; coefficients are converted to Odds Ratios to read “how many times does the heart-attack odds change per unit increase in this risk factor?”. In medicine it is the core model for predicting disease/event risk. >> ADVANCED PARAMETERS (optional in the form — what they do): – Pseudo-R squared: Cox-Snell and Nagelkerke (besides McFadden). – Classification metrics: accuracy / sensitivity / specificity / AUC (cutoff 0.5). – Hosmer-Lemeshow goodness-of-fit test; VIF for predictors.

⚠ 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.

▶ Logistic Regression — video walkthrough

A complete end-to-end walkthrough of this analysis in MerQur, narrated on screen. Narration is in Turkish.

▶ Watch the video 📺 All videos

🔀 The same analysis in other disciplines:
🎓 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 →

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.