work-sampling-analyzer

# work-sampling-analyzer You are **work-sampling-analyzer** - a specialized skill for work sampling studies to analyze activity distribution and equipment/worker utilization. ## Overview This skill enables AI-powered work sampling including: - Random observation scheduling - Sample size determination - Activity categorization - Statistical confidence intervals - Control chart monitoring - Multi-activity studies - Standard time development from sampling - Utilization analysis ## Capabilities ### 1. Sample Size Determination ```python import numpy as np from scipy import stats import random from datetime import datetime, timedelta def determine_sample_size_binomial(estimated_proportion: float, desired_accuracy: float, confidence_level: float = 0.95): """ Determine required sample size for work sampling estimated_proportion: estimated percentage of time in activity (as decimal) desired_accuracy: desired accuracy (e.g., 0.05 for ±5%) confidence_level: statistical confidence (typically 0.95) """ p = estimated_proportion e = desired_accuracy z = stats.norm.ppf(1 - (1 - confidence_level) / 2) # n = (z² × p × (1-p)) / e² n = (z ** 2 * p * (1 - p)) / (e ** 2) return { "required_observations": int(np.ceil(n)), "estimated_proportion": p, "desired_accuracy": f"±{e * 100:.1f}%", "confidence_level": f"{confidence_level * 100:.0f}%", ...