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Multicollinearity means that predictor columns are strongly linearly related. It inflates coefficient variances and can make individual effects imprecise even when a joint test strongly rejects that all corresponding coefficients vanish.

With ${\hat{\sigma }}^2=\Vert Y-X\hat{\beta }{\Vert }^2/(n-p)$, a simultaneous $(1-\alpha )$ confidence region for $\beta$ is

The hat matrixis the orthogonal projection onto the column space of a full-rank design matrix, and the fitted response is $\hat{Y}=HY$.

In R, lm(y ~ 1, data=d) fits an intercept-only linear model, lm(y ~ x1 + x2, data=d) adds quantitative predictors, and a factor predictor is expanded into indicator columns.

Linear regression models a response's conditional expectation as a linear combination of specified predictors. In matrix form,where the design matrix $X$ describes the observations, $\beta$ contains the regression coefficients, and $\epsilon$ contains the errors. The fitted values $X\hat{\beta }$ estimate the response means. The model is linear in its regression coefficients; its columns may include powers, interactions, or other transformed predictors, so polynomial regression can fit curved relationships using the same machinery.

For a concrete example, take five constructed observationsAn ordinary least squares fit with an intercept gives the line $\hat{y}=0.8+x$. Its regression residuals are $(0.2,0.2,-0.8,0.2,0.2)$ and its residual sum of squares is $0.8$. A one-unit increase in the predictor corresponds to a one-unit increase in the fitted response. This is a description of this fitted association; interpreting a change as a causal effect requires the assumptions of a causal inference problem.

The geometric reason for the fit is simple. The possible mean vectors $X\beta$ form the column space of $X$. Ordinary least squares chooses the orthogonal projection of the data vector onto that vector subspace, so the remaining regression residual is orthogonal to every permitted fitted direction. The least-squares normal equations express this orthogonality as $X^T(Y-X\hat{\beta })=0$. The data's explained component and unexplained component are therefore separated by the same geometry used in a Hilbert space.

The optimization definition makes sense for any observed data. Quantifying the uncertainty of the regression coefficients needs additional assumptions about the sampling and errors. With fixed $X$ of full column rank and mean-zero errors with covariance matrix ${\sigma }^{2}I$, ordinary least squares is unbiased and has covariance ${\sigma }^2(X^{T}X{)}^{-1}$; the Gauss-Markov theorem compares it with other linear unbiased estimators. A normal linear model additionally makes Student t-distribution intervals and tests exact in finite samples. Model adequacy and predictive assessment remain relevant even when these calculations are available.

Two historical settings help explain the subject's appeal. Adrien-Marie Legendre's Nouvelles méthodes pour la détermination des orbites des comètes (1805) includes an appendix on the method of least squares: observations give more equations than unknowns, and squared errors provide a criterion for reconciling them. Its astronomical setting shows the motivation for combining imperfect measurements. ^[ref] Francis Galton's Regression Towards Mediocrity in Hereditary Stature (1886) examines the association between parental and offspring heights and the tendency of group averages toward the population average. This is a historical use of the regression name; the modern linear model is a general statistical formulation. ^[ref]

Orthogonal projections of an isotropic Gaussian vector are independent. If $P$ is an orthogonal projection of rank $r$ and $Z\sim N(0,I)$, then

Nonparametric regression estimates a regression function without restricting it to a fixed finite-dimensional family.

density estimation constructs an estimate of an unknown probability density function from sampled observations.

This local phase change is a gauge transformation of the wave function. For real $a$ and a real continuously differentiable $\psi$, multiplication by $e^{ia\psi (x)}$ is a gauge transform that preserves pointwise modulus and mass while shifting the gradient by $ia(\nabla \psi )u$.

For the mass-critical focusing nonlinear Schrödinger equation in dimension $d$, the NLS ground state is the positive radial solution ofIts symmetry orbit consists of the optimizers of the sharp Gagliardo-Nirenberg inequality.

For a radial function $u\in H^1({\mathbb{R}}^d)$,This supplies spatial decay without requiring a weighted moment.

For $i{u}_t+\Delta u+|u{|}^{p-1}u=0$, a smooth real weight $\chi$ defines the localized mass $I_{\chi }(t)=\int \chi |u{|}^2$. For a sufficiently regular decaying solution and a weight with the required integrability,andThese formulas follow by substituting the nonlinear Schrödinger equation and integration by parts. The Hessian controls the gradient contribution; the fourth derivative term records variation of the cutoff.

Choose ${\chi }_R(x)=R^2\chi (x/R)$, with $\chi (x)=|x{|}^2$ near zero and constant outside a larger ball. The interior terms reproduce the virial identity, while the remaining terms are supported in the transition region or controlled by spatial tails. A bounded weight makes $I_{{\chi }_R}$ meaningful for finite mass without requiring finite variance. A blowup proof still needs estimates that control those tail errors.

For the mass-critical focusing nonlinear Schrödinger equation, let $u\in C([0,T_{+});H^1({\mathbb{R}}^d))$ be the maximal forward solution in the standard local $H^1$ theory. Either $T_{+}=\infty$, orBecause Mass conservation for the nonlinear Schrödinger equation fixes $\Vert u(t){\Vert }_2$, this is equivalent to divergence of $\Vert \nabla u(t){\Vert }_2$.

The proof uses a local existence time bounded below for data in a bounded H1 norm ball. If a sequence $t_n\uparrow T_{+}$ had uniformly bounded H1 norms, restart the equation at $t_n$ with that common time interval. Uniqueness joins the restarted solution to the old one and extends it beyond $T_{+}$, contradicting maximality.

The dependence of lifespan on the norm is a hypothesis of this argument. Other critical solution theories may use a spacetime control norm instead, so the stated H1 norm conclusion is attached here to the mass-critical equation and its $H^1$-subcritical local theory.^[ref]

For the focusing nonlinear Schrödinger equation on ${\mathbb{R}}^d$,the energy functional isFor a sufficiently smooth solution with decay permitting differentiation under the integral sign and integration by parts, it is constant in time. Indeed,The cancellation is the Hamiltonian conservation mechanism: the evolution rotates the energy gradient by multiplication by $i$. The focusing sign makes the nonlinear contribution negative, so conserved energy alone need not bound the L2 norm of the gradient.^[ref]

For less regular solutions the displayed computation is only formal. To inherit the identity, approximate by smooth solutions and justify convergence of both energy terms. Convergence in Sobolev space $H^1$ controls the nonlinear term through a suitable Sobolev embedding only in the applicable exponent range, or with additional integrability. An arbitrary weak solution must not be assumed to satisfy the identity solely because the smooth equation does.

For the nonlinear Schrödinger equationwith real-valued $F$, a sufficiently regular, decaying solution with finite L2 norm conservesDifferentiating gives $M^{\prime }(t)=2\mathrm{Re}\int u_t\overline{u}$. Substitute $u_t=i\Delta u+iF(|u{|}^2)u$. Integration by parts makes $\int \Delta u\overline{u}=-\int |\nabla u{|}^2$ real, and $\int F(|u{|}^2)|u{|}^2$ is real as well, so multiplication by $i$ gives zero real part.

The real coefficient is essential to this calculation; phase covariance alone does not imply mass conservation. For an $H^1$ solution obtained by a well-posed approximation theory, the invariant extends from smooth solutions by continuity in the L2 norm.^[ref]

In the convention used here, a Brzozowski NFA has one final state, every state can reach it, and each word labels a path to it from exactly one starting state.

The powerset construction turns an NFA with state set $Q$ into a DFA with state set $\mathcal{P}(Q)$, transitionand accepting subsets that meet the NFA final set.

Without epsilon transitions, defineA word is accepted when the set reached from the initial state meets the final set.

In $R=\mathbb{Z}[\sqrt{-5}]$, the idealhas additive index two. If $I=(a+b\sqrt{-5})$ were principal, multiplication by its generator would givewhich has no solution in integers. Thus $I$ is not principal.

The global phase symmetry of a Dirac field gives the conserved Dirac current $j^{\mu }=\bar{\psi }{\gamma }^{\mu }\psi$.