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	Distinct
disease_progression	numeric	214
bmi	numeric	163
blood_pressure	numeric	100
age	numeric	58
sex	numeric	2
s1	numeric	141
s2	numeric	302
s3	numeric	63
s4	numeric	66
s5	numeric	159
s6	numeric	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: 46 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 Tests10

AI Interpretation9

Visualizations6

Report Generation5

Total to run46 credits

Estimated runtime: ~45-90 seconds

ResultsComputed

3 tests46 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)

significant

r.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)

significant

R².396adj R².393N442p< .001

Adjusted R squared0.393

small

t	vif	coef	term	std err
-9.158	—	-203.623	intercept	22.234
12.089	1.185	8.519	bmi	0.705
6.152	1.185	1.385	blood_pressure	0.225

Predicting Diabetes Progression

Publication-ready report

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