All examples for Distribution Analyzer

IoT Sensor Data with Large Sample

Analyzing sensor readings with 6000 observations to trigger Kolmogorov-Smirnov test instead of Shapiro-Wilk. Demonstrates correct test selection for large samples, handling of zero-inflated error counts, and identification of normally distributed sensor measurements alongside skewed operational metrics.

Input

What you provide to the skill

Analyze a sensor readings dataset with temperature, humidity, vibration, and error_count (6000 rows) to test large-sample normality detection

Output

What the skill generates for you

Distribution Analysis Report

Summary Statistics

Variable Count Mean Median Std Min Max Q1 Q3 IQR Skewness Kurtosis Missing Missing%
temperature 6000 71.9906 72.0059 2.9952 62.2762 83.7787 70.0094 73.9706 3.9612 0.0069 0.0392 0 0
humidity 6000 44.9284 44.8502 8.0399 13.6208 73.2324 39.5512 50.3567 10.8055 -0.0154 0.0523 0 0
vibration 6000 0.0412 0.0289 0.041 0 0.3798 0.0124 0.056 0.0436 2.0395 6.1968 0 0
error_count 6000 0.4598 0 1.2954 0 10 0 0 0 3.1302 9.9363 0 0

Distribution Shapes

Variable Distribution Skewness Kurtosis
temperature Approximately normal 0.0069 0.0392
humidity Approximately normal -0.0154 0.0523
vibration Right-skewed, heavy-tailed 2.0395 6.1968
error_count Right-skewed, heavy-tailed 3.1302 9.9363

Normality Tests

Variable Test Statistic p-value Result
temperature K-S 0.0071 0.922159 Normal
humidity K-S 0.0061 0.976811 Normal
vibration K-S 0.1576 0 Non-normal
error_count K-S 0.4964 0 Non-normal

Transformation Recommendations

Variable Recommendation
temperature None needed - approximately normal
humidity None needed - approximately normal
vibration Log transformation (strong right skew)
error_count Shift + Log transformation
View all examples for Distribution Analyzer

About This Skill

Automated distribution analysis for numeric dataset variables with statistics, visualizations, and transformation recommendations.

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