Statistics Formulas

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A comprehensive collection of statistical formulas, from basic descriptive statistics to advanced inferential methods. Explore each formula for detailed insights and applications 1.

Formula Categories

Descriptive Statistics

Mean (Arithmetic Average)

x̄ = (Σx) / n

Where Σx is the sum of all values, and n is the number of values.

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Standard Deviation

σ = √[(Σ(x - μ)²) / N]

Where μ is the population mean, and N is the population size.

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Variance

σ² = Σ(x - μ)² / N

A measure of how spread out the data is from its mean.

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Probability

Conditional Probability

P(A|B) = P(A∩B) / P(B)

The probability of event A occurring, given that event B has occurred.

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Bayes' Theorem

P(A|B) = [P(B|A) × P(A)] / P(B)

Calculates the probability of a hypothesis given observed evidence.

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Distributions

Normal Distribution PDF

f(x) = (1/σ√2π) × e^(-(x-μ)²/2σ²)

Describes the probability density function of a normal distribution.

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Binomial Probability

P(X=k) = C(n,k) × p^k × (1-p)^(n-k)

Gives the probability of k successes in n trials.

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Statistical Inference

Confidence Interval

x̄ ± (z × σ/√n)

Provides a range within which a population parameter is likely to fall.

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Sample Size Determination

n = (z²σ²) / E²

Calculates the necessary sample size for a desired margin of error.

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Regression

Linear Regression Equation

ŷ = β₀ + β₁x

Models the relationship between a dependent and independent variable.

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Least Squares Regression Line

b₁ = r(Sy/Sx)

Determines the line of best fit for a set of data points.

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Correlation

Pearson Correlation Coefficient

r = Σ((x-x̄)(y-ȳ)) / √[Σ(x-x̄)²Σ(y-ȳ)²]

Measures the strength and direction of a linear relationship.

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Spearman's Rank Correlation

ρ = 1 - (6Σd²) / (n(n² - 1))

Measures the monotonic relationship between two ranked variables.

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