skills.rest
sencersoylu
sencersoyluCOMMUNITY
Explore all skills by this author →
𝕏@birelektronikciGitHub

regression-analysis

Guide linear, logistic, and Cox regression with diagnostics in Python.

Updated Mar 19, 2026
Search Tags:#diagnostics#statistics#regression#model-selection#survival-analysis#biostatistics
One-click install
npx skills add https://github.com/sencersoylu/scholar-flow --skill regression-analysis-sencersoylu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: regression-analysis
Source: https://github.com/sencersoylu/scholar-flow/tree/main/skills/statistics/regression-analysis
Command: npx skills add https://github.com/sencersoylu/scholar-flow --skill regression-analysis-sencersoylu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, lifelines, matplotlib, scipy, statsmodels, scikit-learn, and includes scripts (resource) components.

What problem does it solve?

Regression analysis is a core method for understanding relationships between outcomes and predictors across linear, logistic, and time-to-event contexts. This Skill provides structured protocols, diagnostics, and reporting guidance to ensure rigorous and reproducible analyses.

Core Features & Use Cases

  • Comprehensive coverage of linear, logistic, and Cox proportional hazards regression, including diagnostics, effect size interpretation, and model reporting.
  • Guidance for model selection, assumption checks (linearity, homoscedasticity, multicollinearity, proportional hazards), and result interpretation across disciplines.
  • Use Case: A medical study evaluating multiple biomarkers' associations with a survival outcome, with a full report including hazard ratios, CIs, and model diagnostics.

Quick Start

Run the regression-analysis workflow on your dataset to fit relevant models, perform diagnostics, and generate a summary report.

Frequently Asked Questions about regression-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform Cox proportional hazards regression with diagnostics in Python?▼

Cox proportional hazards regression is supported using the lifelines library, guiding you through model fitting, assumption checks, hazard ratio interpretation, and confidence interval reporting for survival analysis outcomes.

What's the best way to check multicollinearity and linearity assumptions in a regression model?▼

Checking multicollinearity and linearity assumptions is handled by built-in diagnostics using statsmodels and scipy, evaluating predictor relationships, residual plots, and variance inflation factors to validate model assumptions.

Can I run both linear and logistic regression workflows on the same dataset?▼

Linear and logistic regression workflows are both supported for the same dataset, allowing you to fit models, evaluate effect sizes, perform diagnostics, and compare model selection criteria across continuous and binary outcomes.

Does this workflow generate reproducible reports for clinical research studies?▼

Reproducible reports for clinical research studies are generated as a summary output, combining fitted models, diagnostics, hazard ratios, and confidence intervals into a structured Python workflow using pandas and statsmodels.

When do I need survival analysis instead of standard logistic regression?▼

Survival analysis is needed instead of logistic regression when modeling time-to-event data with censoring, utilizing Cox models from lifelines to estimate hazard ratios rather than binary outcome probabilities.

How do I select the best predictors for a biostatistics regression model?▼

Selecting the best predictors for a biostatistics regression model involves applying model selection criteria within the workflow, evaluating diagnostics and effect sizes across various predictors to identify the optimal model.

Related Skills

Explore More Data & Analytics Skills→
CentauriounCentaurioun

tooluniverse-statistical-modeling

Fit biomedical regression models and compute effect estimates with statsmodels and lifelines.

Community
Advanced
rubenslivrubensliv
15

seaborn

Create publication-quality statistical plots from pandas DataFrames using seaborn and matplotlib.

Community
Advanced
Estrella-231Estrella-231

scikit-learn

Builds and evaluates scikit-learn classification, regression, clustering and preprocessing pipelines with mixed numeric/categorical data.

Community
Intermediate
choxoschoxos
6

Bayesian Modeling in R

Specify priors and analyze posterior distributions in R using brms and rstanarm.

Community
Advanced