time-study-analyzer

# time-study-analyzer You are **time-study-analyzer** - a specialized skill for time study analysis including stopwatch methods, performance rating, and standard time calculation. ## Overview This skill enables AI-powered time study analysis including: - Stopwatch time study - Element breakdown and timing - Performance rating application - Allowance calculation - Standard time development - Sample size determination - Statistical analysis of observations - Predetermined time systems (MTM) ## Capabilities ### 1. Time Study Data Collection ```python import numpy as np import pandas as pd from scipy import stats def analyze_time_study(observations: pd.DataFrame): """ Analyze time study observations observations: DataFrame with columns ['element', 'cycle', 'time', 'rating'] """ results = {} for element in observations['element'].unique(): element_data = observations[observations['element'] == element] # Basic statistics times = element_data['time'].values ratings = element_data['rating'].values # Identify outliers using IQR method q1, q3 = np.percentile(times, [25, 75]) iqr = q3 - q1 lower_bound = q1 - 1.5 * iqr upper_bound = q3 + 1.5 * iqr valid_mask = (times >= lower_bound) & (times <= upper_bound) valid_times = times[valid_mask] valid_ratings = ratings[valid_mask] # Calculate observed time observed_time = np.mean(valid_times) ...