[Verse 1] When data points scatter across the field Range tells the story from low to high Subtract the minimum from maximum yield Simple distance that meets the eye But outliers can stretch this measure wide Making range a rough and basic guide [Chorus] Variance squares the deviations Standard deviation takes the root Spread around the mean's vibration These are tools that really compute Remember: variance squared, standard root Measuring scatter is their pursuit [Verse 2] Variance calculates each point's distance From the mean, then squares each gap Sum divided by count for instance Population formula in your lap Sample version uses n minus one Bessel's correction gets the job done [Chorus] Variance squares the deviations Standard deviation takes the root Spread around the mean's vibration These are tools that really compute Remember: variance squared, standard root Measuring scatter is their pursuit [Bridge] Skewness measures asymmetric tails Positive skew leans toward the right Negative skew when left side prevails Zero means symmetric sight Kurtosis checks the peak's height Leptokurtic spikes up tight Platykurtic spreads out flat Mesokurtic normal stat [Verse 3] Standard deviation in same units As original data points you see Variance in squared units Less intuitive for you and me Together they reveal the truth About your dataset's proof [Final Chorus] Variance squares the deviations Standard deviation takes the root Skewness shows the distribution's variations Kurtosis measures where peaks shoot Range, variance, standard root Shape and spread - complete pursuit [Outro] From simple range to complex curves These statistics show what data serves Variability's complete portrait In every analyst's toolkit
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