Factors Associated with Smoking Among In-School Adolescents: Logistic Regression Model

Binary logistic regression modelling outcomes with factors associated with smoking among in-school adolescents, authored by Funmito Omolola Fehintola. The dataset is a 9.5 KB XLS file last updated on April 28, 2026, and is licensed under CC-BY-4.0.

Use Cases

• Train a binary classification model to predict adolescent smoking status based on associated factors.
• Identify key risk factors for smoking initiation among in-school youth as suggested by the regression model.
• Benchmark logistic regression performance on a small-scale public health dataset.
• Conduct secondary analysis on the relationship between modeled factors and smoking outcomes.

Strengths

• Dataset is openly licensed under CC-BY-4.0, permitting broad reuse.
• File size is 9.5 KB, indicating a focused and likely manageable scope.
• Last update timestamp is precisely recorded as 2026-04-28 17:40:07.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare, author Funmito Omolola Fehintola

Collection Method

Likely contains survey or observational data modeled via binary logistic regression.

Time Range

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Freshness

Last updated 2026-04-28 17:40:07; freshness should be verified.

Geography

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