In stellar homology, write , and , with common dimensionless profiles. Integrating hydrostatic equilibrium from the surface, where the pressure is negligible, gives
The profile integral is common to the sequence. Similarly, mass conservation gives . These prove the homology scaling of central stellar pressure:
The ideal gas law consequently gives , with the gravitational and gas constants absorbed into the coefficient.
The radiative diffusion in a star equation has a characteristic gradient . Hence . Using Kramers' opacity law, and comparing fixed-composition homologous models, this becomes
The volume integral of the stellar energy-generation rate instead gives
The dimensionless integrals behind these relations do not depend on along a homologous sequence. In stellar thermal equilibrium the two luminosities agree, proving Kramers homology with a variable nuclear exponent:
For the specified proton–proton chain exponent , the mass factor cancels. Thus
The model captures two important features of the lower main sequence: hydrogen burning primarily uses the proton–proton chain, and gas rather than radiation pressure supplies the principal support. A radiative interior and a Kramers' opacity law approximation are useful near the Sun. It is a simplified local model: the Sun has a convective envelope, the coolest main sequence stars become fully convective, and real opacities and burning exponents vary. In particular a literally constant radius is not a realistic law over the entire lower main sequence.
On this proton–proton chain branch , because and mean molecular weight are fixed. Normalizing to the Sun gives the upper mass estimate
For the specified CNO cycle exponent , the homology relation instead reads . Substituting into the radiative luminosity relation gives
This branch retains the deliberately idealized fully radiative assumptions, even though real CNO cycle cores are often convective.
For a Hertzsprung-Russell diagram, use the Stefan–Boltzmann law, . On the proton–proton chain branch , so . On the CNO cycle branch , giving . Thus the upper branch rises slightly more steeply toward the hot, luminous upper left in a logarithmic Hertzsprung-Russell diagram. The sketch below matches the approximate branches continuously at their respective burning crossovers; the true crossover has both heating terms and is smooth.
The proton–proton chain begins with reactions between hydrogen nuclei, so its specific heating scales as in this approximation. The catalytic CNO cycle involves hydrogen and CNO nuclei, and scales as . Here is the hydrogen mass fraction and the CNO mass fraction, not the total abundance of every metal. Consequently
Keeping fixed while dividing by 256 gives the CNO crossover temperature
The mass estimate uses the unchanged proton–proton chain branch below crossover, together with the stipulated unchanged opacity and mean molecular weight.
The low-CNO proton–proton chain sequence therefore lies on the same locus, but extends to twice the transition mass. Beyond its later crossover the low-CNO CNO cycle sequence has the same logarithmic slope but a different normalization. At a fixed mass where both sequences are CNO-dominated, lowering the nuclear coefficient by 256 gives
Thus those low-CNO models are smaller, hotter and modestly more luminous at the same mass. At the same effective temperature, their CNO branch is lower in luminosity than the solar-composition CNO branch; these comparisons hold different quantities fixed.
Figure 1.
Radiative homology sequences for solar CNO abundance and a CNO abundance reduced by 256, with their different burning crossovers
. Details.
A stellar polytrope has a spatially constant coefficient in the relation ; is its polytropic index. This relates pressure to mass density and need not describe an adiabatic gas. Combining hydrostatic equilibrium with mass conservation eliminates the enclosed mass:
For , the polytropic equation of state gives . Choose the Lane-Emden variables for a stellar polytrope with
Substitution then gives the dimensionless Lane-Emden equation:
A regular stellar centre has , and . In particular the regular central expansion is . For an isolated finite-radius stellar polytrope with negligible external pressure, the surface is the first zero of , and . A core embedded in an envelope can instead end at a positive matching pressure, before this zero.
The integrated Lane-Emden equation is . Thus mass conservation gives the Lane-Emden mass formula directly:
For the proposed functional form, differentiation gives
Choosing removes the extra polynomial factor; equality to then requires . The regular polytrope of index five is therefore
Its enclosed mass and enclosed mean mass density are
There is no finite zero: its isolated radius is infinite. Nevertheless its total mass converges, since the outer mass density falls as :
The zero mean mass density is a limiting volume average, not a statement that the material has zero mass density at finite radius.
To turn this density model into a CNO cycle burning model, use gas-dominated pressure and uniform mean molecular weight. The ideal gas law and the polytropic equation of state then imply . Without this thermal assumption a polytropic equation of state for total pressure does not alone fix the temperature profile. Since the stellar energy-generation rate is power per unit mass, its contribution to luminosity is , giving the luminosity integral of an index-five polytrope:
Put . Then and the dimensionless integral becomes . Evaluation gives
The PDF's power of in the proposed luminosity scaling is a genuine dimensional error: it must be three, rather than two. The volume element supplies ; replacing it by would give power divided by length, and could not be repaired by a dimensionless numerical constant.
The polytrope of index five is unlikely to model a real burning stellar core well. An embedded core has a finite boundary, and a complete isolated star cannot have this infinite radius. More importantly, strongly temperature-sensitive CNO cycle heating concentrates the luminosity centrally and tends to produce a convection zone there. A well-mixed gas-pressure-dominated convective core is closer to an adiabatic stellar polytrope of index than to index five. A finite truncation would also change the integral defining ; its value above pertains to the full mathematical model.
Work at fixed enclosed mass , so the perturbations are fluid Lagrangian perturbations. The equilibrium acceleration vanishes. Varying the factor in the gravitational force contributes , while varying the factor multiplying the acceleration gives no first-order term because the background acceleration is zero. With , the linearized momentum equation is
Conservation of the mass of each displaced shell gives its fractional density change as minus the fractional volume change. Thus
The adiabatic relation is . Use and the stellar hydrostatic equation to turn the mass-coordinate momentum equation into . It follows that the radial stellar pulsation equation is
The stellar adiabatic exponent may vary with radius, so it must remain inside the derivative.
Expand the derivative and multiply by . The self-adjoint differential equation becomes
Hence and in the requested form. Multiplying by a real eigenfunction and integrating gives the boundary term . It vanishes for a regular centre and bounded admissible surface displacement and derivative, since . Therefore the weighted stellar pulsation Rayleigh quotient is
Real coefficients and the symmetric endpoint conditions give the Sturm-Liouville problem; the Rayleigh-Ritz variational principle identifies its minimum with the lowest squared frequency. For complex amplitudes the corresponding quadratic forms use modulus squares, and the real eigenfunctions may be chosen for this calculation.
Integrate the second term of by parts. Its boundary term again vanishes. The remaining integrand is
Since , one has . Substitution proves the positive-square radial pulsation energy identity
If throughout the positive-pressure interior, both terms are nonnegative for every admissible displacement. No negative squared frequency is then possible. Hence radial dynamical instability requires somewhere inside the star.
For the opposite necessary condition, test the quotient with a nonzero constant . It has and
If everywhere and is strictly below on a positive-pressure interval, this is negative. The lowest eigenvalue is at most this negative trial quotient, proving instability. Strict dynamical stability requires the stronger weighted condition
and in particular somewhere. This is the pressure-weighted radial instability criterion viewed as a necessary test for strict stability.
The word “stability” needs a marginal qualification: if throughout, solves the pulsation equation with . The star is marginally stable in this radial sense, so the strict requirement applies to positive fundamental squared frequency, not to a definition that includes a zero-frequency neutral mode. There is no exponentially growing mode in this equality case, but the zero restoring force also permits a secular solution linear in time.
The conditions are useful as rapid tests, but local threshold crossings are not a complete criterion for a mixed exponent profile. If everywhere and exceeds it on a positive-pressure interval, the square identity forces for every nonzero eigenfunction: zero energy would first force constant and then force that constant to vanish. If everywhere with strict inequality on an interval, the constant trial proves instability. When both types of region occur, somewhere does not alone prove instability, and somewhere does not alone prove stability. A negative pressure-weighted average proves instability, while a positive one tests only the homologous displacement; another radial shape may still have negative energy. Determining the mixed case requires minimizing the full weighted stellar pulsation Rayleigh quotient or solving the radial stellar pulsation equation. These are radial adiabatic statements and do not rule out nonradial, convective or nonadiabatic instabilities.
Fix the sign convention first: depth increases downward, while the vertical displacement amplitude is positive upward. Write for the Lagrangian pressure perturbation, and for Eulerian fluid perturbations. Hydrostatic equilibrium gives , and the adiabatic relation gives . Since the vertical displacement vector has component along increasing ,
In the Cowling approximation, horizontal momentum gives . The mass conservation equation and vertical momentum then eliminate the horizontal displacement. The plane-parallel Cowling displacement-pressure equations are
For , the second equation admits and the first reduces to . Thus the plane-parallel stellar f-mode has
Its Eulerian eigenfunctions are and , while . Hence it has no Lagrangian compression even in a stratified star. It decays inward but grows upward, explaining the failure of a pure small-amplitude continuation to .
For the lower part of the matched chromospheric interfacial mode, put , and . The pressure amplitude is of order because the limiting f-mode has identically zero Lagrangian pressure amplitude; the frequency correction perturbs that zero solution. Expand and . The leading equations are
Exclude the lower homogeneous component , which would make grow into the star. Integrating the remaining equation with its integrating factor gives
The accompanying correction is , with the integrand evaluated at . This fixes and determines the lower eigenfunctions to the stated order.
For the upper part, distinguish the rescaled sound speed by writing and . Set , and . To leading order the first displacement equation becomes , while the pressure equation gives . Consequently
The component would give , which diverges upward. Remove it by the upper boundary condition. The leading upper solution is
Integrating with gives the first correction to the upper exponential. The displayed satisfies the required decay only for compatible upper density profiles; integral convergence alone is not the full boundary condition.
To match, define the positive weighted integrals
The lower normalization is and . Continuity of Lagrangian pressure perturbation makes the upper ; continuity of fluid displacement then requires . Since , the upper integral gives . The two matching conditions therefore give
As a sign and normalization check, two incompressible constant-density layers give and , so . This agrees with expansion of the exact interfacial gravity-wave dispersion relation .
For validity, require , convergent weighted integrals, a matching level separating the low-density high-sound-speed atmosphere from the denser envelope, and small accumulated corrections over the regions carrying the matching integrals. The upper expansion also needs . The resulting upper displacement must tend to zero: for example if at great height, its leading amplitude is proportional to , requiring ; the weaker condition merely makes the upper integral converge. Plane geometry, constant and the Cowling approximation must be appropriate on the wavelength scale. The physical mode amplitude must remain small compared with both its wavelength and local background scale heights, so the adiabatic fluid perturbation remains linear. If these requirements fail, neither the matching formula nor continuation of the leading expansions is controlled.
In the dominant central Newtonian gravitational potential, conservation of energy for gas falling from rest at infinity gives , hence with . The characteristic stellar speed has the same radial scaling. For random stellar directions, the cross term in averages to zero, so the typical relative speed is also . With the stated kinetic-energy estimate, an rms convention gives ; another convention changes only the constant .
In the steady spherical inflow approximation, mass conservation gives . Thus
This leading calculation treats the inward gas flux as essentially undepleted over the region used. Substantial distributed removal by stars would make the local flux a function of , an important limitation discussed below.
Insert these scalings in the Bondi--Hoyle--Lyttleton accretion rate. The factors from and from cancel:
This is quadratic accretion in a central Keplerian potential. Write its fixed coefficient as . Integrating gives
The cancellation of radius means that orbital variation of alone does not change this coefficient, provided the assumed steady gas profile, central mass and relative-velocity scaling remain fixed.
Conserve the number of stars along these deterministic mass trajectories. Inverting the map and taking its Jacobian determinant gives
The mass-spectrum transport under quadratic accretion is therefore
For a narrow but nonzero initial mass interval, the mapping stretches the high end especially strongly. If its endpoints are , their evolved ratio is , which can become large as approaches from below. In an intermediate high-mass range with , mapped back into a portion of the initial interval where changes little,
Here is near on the populated side of the initial interval. Finite initial support gives a finite upper mass cutoff at every time before the earliest blow-up; this is an approximate broad-range power law, not a literal infinite tail of a finite pre-blow-up population. A strictly monochromatic initial population remains monochromatic under identical accretion coefficients and does not generate a spectrum.
The limitations of this accretion model are substantial. The star cluster must remain centrally dominated, and both the central mass and gas supply must remain effectively fixed. Stellar capture depletes the gas flux and eventually invalidates the profile. Gas pressure, angular momentum, shocks, radiative feedback and winds can reduce capture relative to the cold Bondi–Hoyle accretion estimate; a broad relative-speed distribution is not faithfully represented by one typical speed, especially because capture scales as . A growing star can perturb the surrounding potential, overlap capture regions, change its orbit through momentum loading, or compete with its neighbors for the same gas. Finally, number conservation excludes mergers, new star formation and stellar losses. The formal finite-time mass divergence cannot persist under a finite available gas mass, so the inferred mass-spectrum slope applies only while these approximations remain adequate.
Integrating the given expansion equation gives , with the integration constant fixed by . Thus the scale factor and Hubble parameter are and . The Newtonian derivation of the flat Friedmann equation gives , so
Use the comoving coordinate and the density contrast . Linearized mass conservation, the peculiar-velocity Euler equation for fluid motion and the Poisson equation for the peculiar gravitational potential are
Differentiate the first equation, substitute the second and use the third. A Fourier component of comoving wavenumber obeys
Since , the normalization is , as printed in the PDF. The resulting linear cosmological density perturbation equation is
To reduce it to a Bessel equation, put , and . As , multiplying the substituted equation by gives
Taking makes the first two coefficients proportional to those in the Bessel equation, and the remaining constant is . Hence
For noninteger , the Bessel density modes of a polytropic expanding universe are
There is an exceptional case within the allowed interval: if is an integer, , so these are not two independent solutions. The full solution then uses , with the Bessel function of the second kind providing the missing branch. For example gives . A basis using is valid for every allowed exponent.
For early times, . The large-argument asymptotic expansion of the Bessel function of the first kind has oscillatory phase and envelope for order ; the Bessel function of the second kind has the same envelope. Thus
This decreases because . At late times , the leading powers are for noninteger order, giving and . The branch gives the same growing power in the integer case. Therefore a generic perturbation grows as ; an initial condition with zero growing-mode coefficient instead has the decaying mode.
To find the growth scale including its dependence on , set . The equation becomes
Substitute . The leading coefficient equation is . The pressure correction to a growing polytropic cosmological mode is consequently
Since and , the correction becomes small when is small compared with . The factor is between two and , giving the stated order-of-magnitude onset condition
For a well-developed asymptotic growing mode, replace by . This is a Jeans instability interpretation: the physical acoustic frequency must be sufficiently low compared with the gravitational expansion scale, or equivalently the physical wavelength must be sufficiently long for self-gravity to overcome pressure support. It is an approximate condition for the dust-like growing branch, not an exact initial-condition-independent instant at which any perturbation begins increasing. The instantaneous sign change of the coefficient in the original equation instead occurs at .
A linear filter of a stationary time series with real coefficients satisfying is . Its filter generating function is the Laurent series , absolutely convergent on , and its filter transfer function is . For a one-sided filter this is a power series; a general two-sided filter need not converge on a larger annulus.
The filter theorem states that if is second-order stationary, then exists in mean square, is second-order stationary, and has mean and covariance
Its spectral measure of a stationary time series is . In particular, if has a time-series spectral density in the convention , then
To prove existence, the norm of a tail of the filter is at most times the sum of the omitted , which tends to zero. Hence finite partial sums converge in . Expectations and covariances pass to the limit, and the covariance double sum is absolutely convergent because . Its formula depends only on , proving stationarity. Insert the spectral representation of and interchange sums and integration; the bound justifies the interchange. The resulting factor is . This proves the spectral statement, including the spectral density transformation under a linear filter without assuming that the input autocovariances are absolutely summable.
For an autoregressive moving-average model, use real polynomials normalized by and first work with the reduced noise-driven representation. If has no zero on the unit circle, the rational function has a Laurent expansion in an annulus containing that circle, with absolutely summable coefficients. Filtering the white noise with it gives a stationary solution. Its time-series spectral density is
Conversely, for coprime , a unit-circle zero of would make this ratio nonintegrable, since the numerator does not vanish there; thus there is no finite-variance stationary noise-driven solution. A stationary solution can use a bilateral filter when autoregressive roots lie inside the unit circle. The stronger causal condition, meaning dependence only on present and past noise, requires every zero of strictly outside the unit disk. Common factors must be handled before applying these conditions; a cancelled unit-root factor can also allow additional deterministic stationary components, which are outside the minimal noise-driven convention.
Identifiability means that different admissible parameters cannot produce the same stationary process law. In this zero-mean Gaussian setting, its law is determined by its covariance, equivalently its spectrum. Unrestricted ARMA coefficients are not identifiable from that law: if a moving-average factor has root inside the disk, then on ,
Reflecting the root outside and multiplying the innovation variance by preserves the spectrum, and therefore the Gaussian law. Common polynomial factors and arbitrary overall scaling give further redundancy. A regular canonical identifiable parameterization requires constant coefficients one, coprime polynomials of their stated minimal orders, all zeros of both and outside the closed unit disk, and .
Here is a proof of canonical identifiability of Gaussian ARMA models. Suppose two such models have equal spectra and write their innovation standard deviations as . The ratio and its reciprocal are analytic on a neighbourhood of the closed unit disk, with modulus one on its boundary. The maximum modulus principle applied to both makes throughout the disk, so is constant. Its value at zero is the positive number , forcing and . Therefore ; coprimality and the normalization at zero force equality of both polynomials. This proves the claimed parameter uniqueness within that canonical class.
In this class, is analytic on a disk of radius greater than one. Its Taylor series coefficients are absolutely summable, so
This is an invertible time-series representation, proved by composition of the stable filters. Causality makes orthogonal to past observations; invertibility makes past noises recoverable from past observations. Their closed past linear spans agree, so this noise is the linear innovation and the one-sided expansion is the Wold representation. Mere stationarity without the stated canonical restrictions does not imply invertibility of the originally written noise representation.
For this one-sided canonical representation, . Comparing coefficients gives the ARMA coefficient recursions
Thus for exactly the range asked for,
The characteristic rates are reciprocals of the zeros of . Partial fractions therefore express the tail as sums of , where and the degree of is at most one less than the corresponding root multiplicity. Hence the coefficients decay exponentially, with possible polynomial and damped oscillatory factors. If , they instead vanish after . For a noncausal or noninvertible original specification, the Wold coefficients refer to the canonical spectral factor; using the original driving noise as the Wold innovation without checking these restrictions would be incorrect.
The autocovariance is . Analyticity of on a disk of radius gives for a slightly smaller radius than its first pole, and consequently . Moreover, multiply the ARMA equation at time by and take expectations. For , all noises with are later than and orthogonal to it. For this gives
The covariance tail therefore has the same characteristic exponential rates and multiplicity patterns as the filter coefficients, subject to possible cancellation of amplitudes. Pure moving averages have an exact covariance cutoff.
For the final process, factor the polynomials:
The common factor has its zero at , so it can be cancelled as a stable filter. Explicitly, let ; the original equation gives . The second moments of are uniformly bounded because those of and are. Iterating gives , whose norm tends to zero as , so in mean square. Thus
Summing the covariance series gives
The process is a causal stationary autoregressive process of order one, with alternating-sign geometric correlations, and is invertible. The displayed ARMA(2,1) description is nonminimal because of its common factor; its minimal identifiable class is ARMA(1,0).
For a weakly stationary process with finite second moments, let and , independent of . If , its autocorrelation function is . Given observations , one usual sample autocovariance function is
The correlogram plots against lag . The partial autocorrelation function at lag is the correlation between and after linearly projecting each onto the intervening observations. Its sample version can be calculated by solving the Yule-Walker regression equations
then taking the last coefficient . Equivalently, use the corresponding residual correlation from the sample regressions, with a consistent covariance convention.
For a minimal causal autoregressive model of order , the population partial autocorrelation function is zero after lag , while the autocorrelation function follows an exponentially decaying or damped oscillatory tail. For a moving-average model of order , its autocovariance and hence autocorrelation are zero after lag , because observations farther apart share no driving-noise terms; its partial correlations usually tail off. These order identification by autocorrelation cutoffs patterns suggest AR or MA candidate orders. Sample cutoffs are approximate and should be judged with sampling uncertainty and residual diagnostics; the familiar white-noise bands near are a null approximation, not universal confidence limits for a dependent series.
For the trend problem, write a symmetric estimator as
Reproducing arbitrary constant and quadratic trends requires and ; the linear-trend condition holds automatically by symmetry. These constraints follow by expanding for a trend . Thus , . Since the errors are uncorrelated and the estimator is unbiased, its mean squared error is
The strictly convex quadratic is minimized at , giving the five-point symmetric quadratic trend smoother
This weighted moving average allows negative coefficients; a convex average could not cancel the quadratic moment.
For three points, symmetry gives weights . Unbiasedness for every quadratic requires and , so the only solution is . Thus there is no nontrivial three-point quadratic-preserving smoother. The literal claim that there is no unbiased estimator at all is false, since is unbiased with error variance . This is the three-point quadratic reproduction forces the identity filter distinction.
For seasonal extraction by a centred moving average, use the additive decomposition , where has period and its values over one period sum to zero. An average covering a full period removes the seasonal component and approximately retains a slowly changing trend. If is odd, take the centred length- average; if is even, centre it by averaging two adjacent length- averages, giving half weights at the two endpoints. Subtract this trend estimate from the data, average residuals within each seasonal phase over different cycles, and centre the resulting phase estimates to sum to zero. For a multiplicative decomposition, use ratios and normalize the phase estimates to average one.
Successive moving averages compose by convolution of their weights. Even an initially uncorrelated noise series becomes correlated: for weights , the filtered noise has covariance . Further smoothing changes this covariance again and can turn random variation into apparently persistent patterns while obscuring genuine short-scale features. Spectrally, repeated use of a transfer function multiplies the spectrum by after applications, so repeated smoothing is not harmless repetition of the same estimate.
In simple exponential smoothing, choose and update . Iteration gives , so older observations receive exponentially decreasing weights. Forecast for every . This is appropriate for a locally stable level with no persistent trend or seasonality.
For a local linear trend, double exponential smoothing supplies level and slope. Holt's linear trend method uses
Another standard convention is Brown's double exponential smoothing: smooth twice with the same gain, and , then put , and forecast . These variants handle a nonseasonal trend; seasonal effects need separate modelling, and a long-horizon linear extrapolation assumes the current local slope persists.
Let and let an approximate experimental design specify the proportions of observations allocated to points of the design region. Its normalized information matrix of an experimental design is
For independent equal-variance observations realizing those proportions, , and the least-squares estimator has covariance matrix . A D-optimal design maximizes , equivalently minimizing the determinant of this covariance at fixed . A G-optimal design minimizes the largest variance of a fitted regression mean over the design region, equivalently
where is the design sensitivity function. This is the variance of the estimated mean multiplied by , not the variance of a new noisy observation.
For continuous regressors spanning a -dimensional parameter space on a compact design region, the general equivalence theorem for optimal design states that, among approximate designs with nonsingular information matrices, the following are equivalent: being a D-optimal design, being a G-optimal design, and
At every support point of an optimal design, . To see the criterion, the sensitivity average is , so the supremum is at least . Adding an infinitesimal allocation at has directional derivative for . At a D-optimum each derivative is nonpositive. Conversely, if all sensitivities are at most , the tangent bound for the concave function shows that no competing design increases it. A nonsingular D-optimum exists by the spanning assumption and compactness of the information-matrix set, so the smallest possible G-value is also . Equality at support points follows from the average. The theorem refers to the full approximate-design class; constrained integer allocations or mandated augmentations require separate comparison.
For the linear mean , take . Equal replication at the four corners gives
All odd corner sums and the sum of vanish, while each squared regressor sum is . If is the mean of the observations at , , the least-squares normal equations give
In particular the intercept is the average of the four cell means, and each slope is the average of its sign-weighted cell means. Independence and equal error variance suffice for this covariance formula; normal errors are not needed for it.
The normalized corner design has , so its sensitivity is
with equality at all four corners. Since , the general equivalence theorem for optimal design proves that the equally weighted corner design is D-optimal and G-optimal.
For the augmentation, all added observations share the regressor . The enlarged design matrix satisfies
By the matrix determinant lemma,
For the permitted nine points, the determinant is at the center, at an edge midpoint and at a corner. Therefore
All four corners tie for the best mandated single-point augmentation. This is optimality among the specified augmented designs, not unrestricted D-optimality with observations: the resulting normalized determinant is , whereas equal corner proportions have determinant one. The restriction that the extra runs all occur at one point is what prevents recovering equal proportions.
Code each factor level by . A nonconstant factorial contrast indexed by a nonempty factor set has sign column . Flipping any coordinate in reverses that sign and pairs the treatment combinations, so exactly have each sign. Allocating the two signs to two experimental blocks therefore divides the full factorial design into equal experimental blocks. The contrast is confounded with experimental blocks because its column is constant within each experimental block: changing its coefficient and compensating by the two experimental block intercepts leaves every fitted value unchanged. It has no information separate from experimental block differences. Randomize the allocation of the two groups to days and the run order within days.
Write for temperature, pressure and catalyst, respectively, with low levels and catalyst one coded minus. In the supplied allocation, on day one and on day two. Thus the three-factor interaction is confounded with days. Every other nonconstant factorial column is orthogonal to the day contrast, because its product with is a nonconstant sign column on the full cube and consequently sums to zero.
If all interactions are negligible, the fitted model contains an intercept, one day contrast and the three main effects. The corrected total statistical degrees of freedom partition as
The three residual statistical degrees of freedom are the unused two-factor interaction columns , pooled as error under the stated negligible-interaction assumption. There is no independent pure-error replication of treatment combinations.
For four days with two runs each, all three main effects can be estimated. One regular allocation pairs each treatment combination with its complete opposite:
The block-generating columns can be and ; their product is also constant in experimental blocks. Thus span the three experimental block contrasts, while and vary within experimental blocks. The main effects remain mutually orthogonal and separate from the experimental block effects; with an additive main-effect model there is one residual degree of freedom, represented by .
For the further request about , the distinction between regular confounding and unrestricted pairing is important. A regular four-block allocation cannot retain all three main effects and . Its three confounded columns form a rank-two defining subgroup. To preserve main effects, these columns must lie in . If the subgroup includes and a two-factor column, their product is a main-effect column, which is forbidden. Hence the only permissible nonidentity subgroup is , and it necessarily confounds . This proves impossibility in the usual regular factorial-blocking framework, rather than relying on a count of available statistical degrees of freedom.
The literal request does not explicitly restrict arrangements to regular experimental blocks. If arbitrary pairings are permitted, a nonregular construction can estimate together, assuming the other interactions are omitted from the model. An explicit allocation is
Every treatment combination occurs exactly once. Fit one unrestricted intercept per day and the four coefficients . By estimability from within-block differences, subtracting the second run from the first in each experimental block removes all day effects and yields the coefficient matrix
Thus all four coefficients are uniquely determined from the four day differences. In this broader design class the answer is yes, under the reduced four-effect model. The resulting fit is saturated: four experimental block intercepts plus four treatment coefficients use all eight observations, leaving no residual estimate of error variance. Replication or external error information would be needed for inference. This does not contradict the impossibility for regular orthogonal confounding, and it does not claim simultaneous unbiased estimation when arbitrary additional interactions are retained.

Pinned article: Introduction to the OurBigBook Project

Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
We have two killer features:
  1. topics: topics group articles by different users with the same title, e.g. here is the topic for the "Fundamental Theorem of Calculus" ourbigbook.com/go/topic/fundamental-theorem-of-calculus
    Articles of different users are sorted by upvote within each article page. This feature is a bit like:
    • a Wikipedia where each user can have their own version of each article
    • a Q&A website like Stack Overflow, where multiple people can give their views on a given topic, and the best ones are sorted by upvote. Except you don't need to wait for someone to ask first, and any topic goes, no matter how narrow or broad
    This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
    Figure 1.
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    Figure 5. . You can also edit articles on the Web editor without installing anything locally.
    Video 3.
    Edit locally and publish demo
    . Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.
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