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The 24 pages that link to Cumulative distribution function, each with the reason it gives.

• Random variableBroader topic: It records the random variable's distribution through threshold probabilities.

• Probability distributionRelated: It fully specifies a distribution and works for discrete, continuous, and mixed cases.

• Probability density functionRelated: Integrating the density up to a value produces this function.

• Normal distributionRelated: It converts normal-curve areas into probabilities and quantiles.

• Sampling (statistics)Related: Inverse transform sampling uses it to map uniform draws onto target-distribution values.

• MedianRelated: The distribution median is located where cumulative probability reaches or straddles one half.

• Independent and identically distributed random variablesBroader topic: Identical distributions require the variables' cumulative distribution functions to coincide.

• Probability mass functionRelated: Summing masses up to a value produces the cumulative distribution function.

• Exponential distributionRelated: It converts an exponential waiting-time threshold into a probability of an event occurring by then.

• Convergence in distributionRelated: The defining test compares these functions at every continuity point of the limit.

• Bernoulli distributionRelated: Its two jumps encode the Bernoulli variable’s probabilities at 0 and 1.

• Empirical distribution functionNarrower topic: The empirical distribution function is the cumulative distribution function of the sample’s discrete probability measure.

• Beta distributionRelated: It converts the beta density into interval probabilities and quantiles.

• Gaussian distributionRelated: It converts the Gaussian density into probabilities and quantiles.

• Independent random variablesRelated: For any variables, independence can be tested by factoring their joint cumulative distribution function.

• Absolute continuityRelated: Absolute continuity distinguishes distributions with densities from those containing singular probability.

• Gutenberg–Richter lawRelated: The law commonly describes cumulative counts above magnitude thresholds.

• Random vectorRelated: Its multivariate form describes probabilities across coordinate-wise lower regions.

• Lévy's continuity theoremRelated: Weak convergence can be checked through these functions at continuity points.

• Slutsky's theoremRelated: Its pointwise behavior at continuity points defines convergence in distribution.

• Continuous uniform distributionRelated: For this distribution, cumulative probability increases linearly within the interval.

• Glivenko–Cantelli theoremNarrower topic: The theorem’s population target is this function.

• Limits of integrationRelated: Probability over an interval is computed by integrating a density between its endpoints.

• 68–95–99.7 ruleRelated: Subtracting its values at each pair of bounds gives the probability within each symmetric interval.