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Descriptive Statistics

Descriptive Statistics Calculator

Calculate mean, variance, standard deviation, and other descriptive statistics for your sample data. Learn the mathematical foundations behind each measure.

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Enter your sample data to calculate descriptive statistics

Mathematical Formulas

Understanding the mathematical foundations behind descriptive statistics

Sample Mean (x̄)

Formula:

x̄ = (Σ xᵢ) / n

Explanation:

The arithmetic average of all sample values. Sum all values and divide by the number of observations.

Usage:

Most common measure of central tendency, sensitive to outliers.

Sample Variance (s²)

Formula:

s² = Σ(xᵢ - x̄)² / (n - 1)

Explanation:

Measures the average squared deviation from the mean. Uses n-1 (Bessel's correction) for unbiased estimation.

Usage:

Unbiased estimator of population variance, fundamental for statistical inference.

Population Variance (σ²)

Formula:

σ² = Σ(xᵢ - x̄)² / n

Explanation:

Measures variability using all n observations in the denominator. Used when treating data as the entire population.

Usage:

Biased estimator when used on samples, but appropriate for population data.

Standard Deviation

Formula:

s = √s² or σ = √σ²

Explanation:

Square root of variance, measures spread in the same units as the original data.

Usage:

Easier to interpret than variance as it's in the same scale as the data.

Median

Formula:

Middle value when data is ordered

Explanation:

For odd n: middle value. For even n: average of two middle values.

Usage:

Robust measure of central tendency, not affected by outliers.

Theoretical Background

Key concepts and applications in descriptive statistics

Central Tendency Measures
  • Mean: Most common, but sensitive to outliers
  • Median: Robust to outliers, represents the middle value
  • Mode: Most frequently occurring value(s)
  • Choose based on data distribution and presence of outliers
Variability Measures
  • Range: Difference between max and min values
  • Variance: Average squared deviation from mean
  • Standard Deviation: Square root of variance
  • IQR: Difference between 75th and 25th percentiles
Distribution Shape
  • Skewness: Measures asymmetry of the distribution
  • Positive skew: tail extends to the right
  • Negative skew: tail extends to the left
  • Kurtosis: Measures tail heaviness compared to normal distribution
Statistical Applications
  • Quality control: Monitor process variation
  • Research: Describe sample characteristics
  • Finance: Analyze risk and returns
  • Foundation for inferential statistics
Key Insights & Best Practices

When to Use Each Measure:

  • Mean: Symmetric distributions, no outliers
  • Median: Skewed distributions, presence of outliers
  • Sample variance: Making inferences about population
  • Population variance: Describing the actual dataset

Important Considerations:

  • • Sample size affects reliability of estimates
  • • Bessel's correction (n-1) provides unbiased variance
  • • Check for outliers before choosing measures
  • • Consider the context and purpose of analysis