How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
and
Statement
For finite real random variables , Variance is nonnegative, and every constant random variable has variance zero. Zero variance forces equality to the mean on every positive-weight outcome, but not necessarily on zero-weight outcomes.
Facts & Assumptions
Given: Real random variables on a finite probability space.
Expectation is linear for every finite family, without independence (Expectation is linear for every finite family of random variables, without any independence hypothesis).
Variance and covariance are expectations of the displayed centred square and product (Variance, standard deviation, and covariance on a finite probability space).
Proof
Expand and apply linearity to obtain .
Expanding and applying linearity gives the covariance identity.
The centred square is pointwise nonnegative, so variance is nonnegative; if is constant it vanishes identically. If the variance is zero, every positive-weight centred-square summand is zero, while a zero-weight outcome is unrestricted.
Steps 1.1, 1.2, and 1.3 give all claims.
Depends on
Used by
- Uncorrelated finite random variables need not be independent Counterexample
- First- and second-moment bounds for a nonempty Bernoulli random subset Example
- A Bernoulli(p) variable has mean p and variance p(1-p); a binomial(n,p) variable has mean np and variance np(1-p) Lemma
- Covariance is symmetric and bilinear in finite linear combinations Theorem
Dependency tree · next 3 levels
Direct dependencies and their dependencies through the next three levels: 33 results over 12 levels. An arrow runs from a result to what uses it, and this result sits at the bottom with a heavier outline. Click the chart to enlarge it.
Sources
- C. M. Grinstead and J. L. Snell, Introduction to Probability, 2nd ed., Section 6.2 (standard reference, not scraped)
- H. Pishro-Nik, Introduction to Probability, Statistics, and Random Processes, Section 5.3.1 (standard reference, not scraped)