Spelling suggestions: "subject:"aanalysis off variance"" "subject:"aanalysis oof variance""
1 
Testing the homogeneity of the two variances of a normal bivariate populationJuico, Yolanda T January 2010 (has links)
Photocopy of typescript. / Digitized by Kansas Correctional Industries

2 
The variate difference method /Siddiqui, Asim Jamal. January 1978 (has links)
No description available.

3 
An analysis of the factors generating the variance between the budgeted and actual operating results of the Naval Aviation Depot at North Island, CaliforniaCurran, Thomas. Schimpff, Joshua. January 2008 (has links) (PDF)
"Submitted in partial fulfillment of the requirements for the degree of Master of Business Administration from the Naval Postgraduate School, June 2008." / Advisor(s): Euske, Kenneth J. ; Mutty, John E. "June 2008." "MBA professional report"Cover. Description based on title screen as viewed on August 8, 2008. Includes bibliographical references (p. 69). Also available in print.

4 
A ksample Wilcoxon Rank Test for the umbrella alternatives.Mack, Gregory Allen, January 1977 (has links)
Thesis (Ph. D.)Ohio State University, 1977. / Includes vita. Includes bibliographical references (leaves 136138). Available online via OhioLINK's ETD Center

5 
Robustness of multivariate mixed model ANOVAProsser, Robert James January 1985 (has links)
In experimental or quasiexperimental studies in which a repeated measures design is used, it is common to obtain scores on several dependent variables on each measurement occasion. Multivariate mixed model (MMM) analysis of variance (Thomas, 1983) is a recently developed alternative to the MANOVA procedure (Bock, 1975; Timm, 1980) for testing multivariate hypotheses concerning effects of a repeated factor (called occasions in this study) and interaction between repeated and nonrepeated factors (termed groupbyoccasion interaction here). If a condition derived by Thomas (1983), multivariate multisample sphericity (MMS), regarding the equality and structure of orthonormalized population covariance matrices is satisfied (given multivariate normality and independence for distributions of subjects' scores), valid likelihoodratio MMM tests of groupbyoccasion interaction and occasions hypotheses are possible. To date, no information has been available concerning actual (empirical) levels of significance of such tests when the MMS condition is violated. This study was conducted to begin to provide such information.
Departure from the MMS condition can be classified into three types— termed departures of types A, B, and C respectively:
(A) the covariance matrix for population ℊ (ℊ = 1,...G), when orthonormalized, has an equaldiagonalblock form but the resulting matrix for population ℊ is unequal to the resulting matrix for population ℊ' (ℊ ≠ ℊ');
(B) the G populations' orthonormalized covariance matrices are equal, but the matrix common to the populations does not have equaldiagonalblock structure; or
(C) one or more populations has an orthonormalized covariance matrix which does not have equaldiagonalblock structure and two or more populations have unequal orthonormalized matrices.
In this study, Monte Carlo procedures were used to examine the effect of each type of violation in turn on the Type I error rates of multivariate mixed model tests of groupbyoccasion interaction and occasions null hypotheses. For each form of violation, experiments modelling several levels of severity were simulated. In these experiments: (a) the number of measured variables was two; (b) the number of measurement occasions was three; (c) the number of populations sampled was two or three; (d) the ratio of average sample size to number of measured variables was six or 12; and (e) the sample size ratios were 1:1 and 1:2 when G was two, and 1:1:1 and 1:1:2 when G was three. In experiments modelling violations of types A and C, the effects of negative and positive sampling were studied.
When type A violations were modelled and samples were equal in size, actual Type I error rates did not differ significantly from nominal levels for tests of either hypothesis
except under the most severe level of violation. In type A experiments using unequal groups in which the largest sample was drawn from the population whose orthogonalized covariance matrix has the smallest determinant (negative sampling), actual Type I error rates were significantly higher than nominal rates for tests of both hypotheses and for all levels of violation. In contrast, empirical levels of significance were significantly lower than nominal rates in type A experiments in which the largest sample was drawn from the population whose orthonormalized covariance matrix had the largest determinant (positive sampling).
Tests of both hypotheses tended to be liberal in experiments which modelled type B violations. No strong relationships were observed between actual Type I error rates and any of: severity of violation, number of groups, ratio of average sample size to number of variables, and relative sizes of samples.
In equalgroups experiments modelling type C violations in which the orthonormalized
pooled covariance matrix departed at the more severe level from equaldiagonalblock form, actual Type I error rates for tests of both hypotheses tended to be liberal. Findings were more complex under the less severe level of structural departure. Empirical significance levels did not vary with the degree of interpopulation heterogeneity of orthonormalized covariance matrices.
In type C experiments modelling negative sampling, tests of both hypotheses
tended to be liberal. Degree of structural departure did not appear to influence actual Type I error rates but degree of interpopulation heterogeneity did. Actual Type I error rates in type C experiments modelling positive sampling were apparently related to the number of groups. When two populations were sampled, both tests tended to be conservative,
while for three groups, the results were more complex. In general, under all types of violation the ratio of average group size to number of variables did not greatly affect actual Type I error rates.
The report concludes with suggestions for practitioners considering use of the MMM procedure based upon the findings and recommends four avenues for future research on Type I error robustness of MMM analysis of variance. The matrix pool and computer programs used in the simulations are included in appendices. / Education, Faculty of / Educational and Counselling Psychology, and Special Education (ECPS), Department of / Graduate

6 
The variate difference method /Siddiqui, Asim Jamal. January 1978 (has links)
No description available.

7 
Mathematical and empirical examinations of some epidemiological procedures /Han, Younghun. Chan, Wenyaw, Chen, LinAn Kapadia, Asha Seth, Risser, Jan Mary Hale. January 2008 (has links)
Thesis (Ph. D.)University of Texas Health Science Center at Houston, School of Public Health, 2008. / "May 2008" Source: Dissertation Abstracts International, Volume: 6902, Section: B, page: 1084. Adviser: Wenyaw Chan. Includes bibliographical references (leaves 3940).

8 
Constrained estimation in multiple groups covariance structure model.January 1981 (has links)
by Kwokleung Tsui. / Thesis (M.Phil.)Chinese University of Hong Kong, 1981. / Bibliography: leaves 3941.

9 
A comparison of two estimators of the variance in the twofactor multiplicative interaction modelWasserstein, Ronald Lee January 2010 (has links)
Typescript (photocopy). / Digitized by Kansas Correctional Industries

10 
Split plot designsEstolano, Marcial Perez January 2010 (has links)
Typescript (photocopy). / Digitized by Kansas Correctional Industries

Page generated in 0.1243 seconds