Read-only sample study. A complete walkthrough — from problem statement to a validated report — using diabetes.csv (442 rows × 11 cols). Sign in to run your own.
Sample study · correlational design
Predicting Diabetes Progression
Research information
Statement of the problem
This study examines baseline clinical measurements and how well they predict one-year disease progression in patients with diabetes.
Objectives
- To describe the disease_progression, bmi, and blood_pressure of the patients.
- To determine the relationship between bmi and disease_progression.
- To predict disease_progression from bmi and blood_pressure.
Dataset
diabetes.csv442 rows × 11 cols
| Column | Kind | Missing | Distinct |
|---|---|---|---|
| disease_progression | numeric | 0 | 214 |
| bmi | numeric | 0 | 163 |
| blood_pressure | numeric | 0 | 100 |
| age | numeric | 0 | 58 |
| sex | numeric | 0 | 2 |
| s1 | numeric | 0 | 141 |
| s2 | numeric | 0 | 302 |
| s3 | numeric | 0 | 63 |
| s4 | numeric | 0 | 66 |
| s5 | numeric | 0 | 159 |
| s6 | numeric | 0 | 56 |
Feasibility & estimateFree
✓ Feasibledesign: correlational · via heuristic
Detected a correlational design with 11 analyzable variable(s) and 3 analytical question(s). Recommended 3 statistical test(s). Estimated cost: 50 credits.
Recommended analysis
To describe the disease_progression, bmi, and blood_pressure of the patients.
Descriptive Statistics(disease_progression, bmi, blood_pressure)
To determine the relationship between bmi and disease_progression.
Pearson Correlation(disease_progression, bmi)
To predict disease_progression from bmi and blood_pressure.
Multiple Linear Regression(disease_progression, bmi, blood_pressure)
Dataset Complexity16
Statistical Tests14
AI Interpretation9
Visualizations6
Report Generation5
Total to run50 credits
Estimated runtime: ~45-90 seconds
ResultsComputed
3 tests50 credits
Descriptive Statistics (disease_progression, bmi, blood_pressure)
| n | max | min | std | mean | mode | range | median | variable | variance |
|---|---|---|---|---|---|---|---|---|---|
| 442 | 346 | 25 | 77.093 | 152.133 | 72 | 321 | 140.500 | disease_progression | 5943.331 |
| 442 | 42.200 | 18 | 4.418 | 26.376 | 23.500 | 24.200 | 25.700 | bmi | 19.520 |
| 442 | 133 | 62 | 13.831 | 94.647 | 83 | 71 | 93 | blood_pressure | 191.304 |
Spearman Rank Correlation (disease_progression, bmi)
significantr.561R².315N442p< .001
Spearman r0.561
large
The result is statistically significant (p = 0.0000 < 0.05).
Spearman Rank Correlation was used instead of the Pearson Correlation because the latter's assumption(s) were not met (Normality of disease_progression (Shapiro-Wilk), Normality of bmi (Shapiro-Wilk)).
Multiple Linear Regression (disease_progression, bmi, blood_pressure)
significantR².396adj R².393N442p< .001
Adjusted R squared0.393
small
| t | vif | coef | term | p value | std err |
|---|---|---|---|---|---|
| -9.158 | — | -203.623 | intercept | 0.000 | 22.234 |
| 12.089 | 1.185 | 8.519 | bmi | 0.000 | 0.705 |
| 6.152 | 1.185 | 1.385 | blood_pressure | 0.000 | 0.225 |
The result is statistically significant (p = 0.0000 < 0.05).
Report
Publication-ready report
Tests used · Results · Discussion — generated from the computed statistics.
Numeric-claim check passed · 15 numbers verified · prose: llm:gemini
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