The analysis of variance / Henry Scheffé
Scheffé, Henry.
Material type: Book; Format: print Series: Wiley classics Library (John Wiley & Sons, Inc).Publisher: New York : John Wiley & Sons, Ltd., 1999ISBN: 0-471-34505-9.Subject(s): Análisis de varianzaItem type | Home library | Call number | Status | Loan | Date due | Barcode | Item holds | Course reserves |
---|---|---|---|---|---|---|---|---|
04. BIBLIOTECA CIENCIAS DE LA SALUD | 1209/SCH (Browse shelf) | Shelving location | Bibliomaps^{®} | BIBLIOG. RECOM. | 374067503X |
METODOLOGIA DE INVESTIGACION BIOMEDICA Y TICS GRADO EN MEDICINA Asignatura actualizada 2017-2018 |
|||
Monografías | 05. BIBLIOTECA FAC. ENFERMERÍA Y FISIOTERAPIA | 1209/SCH (Browse shelf) | Shelving location | Bibliomaps^{®} | BIBLIOG. RECOM. | 3740674974 |
Enhanced descriptions from Syndetics:
Originally published in 1959, this classic volume has had a major impact on generations of statisticians. Newly issued in the Wiley Classics Series, the book examines the basic theory of analysis of variance by considering several different mathematical models. Part I looks at the theory of fixed-effects models with independent observations of equal variance, while Part II begins to explore the analysis of variance in the case of other models.
Originally published in 1959, this classic volume has had a major impact on generations of statisticians. Newly issued in the Wiley Classics Series, the book examines the basic theory of analysis of variance by considering several different mathematical models. Part I looks at the theory of fixed-effects models with independent observations of equal variance, while Part II begins to explore the analysis of variance in the case of other models.
Table of contents provided by Syndetics
- Part I. The Analysis of Variance in the Case of Models with Fixed Effects and Independent Observations of Equal Variance
- Chapter 1 Point Estimation
- 1.1 Introduction (p. 3)
- 1.2 Mathematical models (p. 4)
- 1.3 Least-squares estimates and normal equations (p. 8)
- 1.4 Estimable functions. The Gauss-Markoff theorem (p. 13)
- 1.5 Reduction of the case where the observations have known correlations and known ratios of variances (p. 19)
- 1.6 The canonical form of the underlying assumptions [Omega]. The mean square for error (p. 21)
- Problems (p. 24)
- Chapter 2 Construction of Confidence Ellipsoids and Tests in the General Case Under Normal Theory
- 2.1 Underlying assumptions [Omega] and distribution of point estimates under [Omega] (p. 25)
- 2.2 Notation for certain tabled distributions (p. 27)
- 2.3 Confidence ellipsoids and confidence intervals for estimable functions (p. 28)
- 2.4 Test of hypothesis H derived from confidence ellipsoid (p. 31)
- 2.5 Test derived from likelihood ratio. The statistic J (p. 32)
- 2.6 Canonical form of [Omega] and H. Distribution of J (p. 37)
- 2.7 Equivalence of the two tests (p. 39)
- 2.8 Charts and tables for the power of the F-test (p. 41)
- 2.9 Geometric interpretation of J. Orthogonality relations (p. 42)
- 2.10 Optimum properties of the F-test (p. 46)
- Problems (p. 51)
- Chapter 3 The One-Way Layout. Multiple Comparison
- 3.1 The one-way layout (p. 55)
- 3.2 An illustration of the theory of estimable functions (p. 60)
- 3.3 An example of power calculations (p. 62)
- 3.4 Contrasts. The S-method of judging all contrasts (p. 66)
- 3.5 The S-method of multiple comparison, general case (p. 68)
- 3.6 The T-method of multiple comparison (p. 73)
- 3.7 Comparison of the S- and T-methods. Other multiple-comparison methods (p. 75)
- 3.8 Comparison of variances (p. 83)
- Problems (p. 87)
- Chapter 4 The Complete Two, Three, and Higher-Way Layouts. Partitioning a Sum of Squares
- 4.1 The two-way layout. Interaction (p. 90)
- 4.2 The two-way layout with one observation per cell (p. 98)
- 4.3 The two-way layout with equal numbers of observations in the cells (p. 106)
- 4.4 The two-way layout with unequal numbers of observations in the cells (p. 112)
- 4.5 The three-way layout (p. 119)
- 4.6 Formal analysis of variance. Partition of the total sum of squares (p. 124)
- 4.7 Partitioning a sum of squares more generally (p. 127)
- 4.8 Interactions in the two-way layout with one observation per cell (p. 129)
- Problems (p. 137)
- Chapter 5 Some Incomplete Layouts: Latin Squares, Incomplete Blocks, and Nested Designs
- 5.1 Latin squares (p. 147)
- 5.2 Incomplete blocks (p. 160)
- 5.3 Nested designs (p. 178)
- Problems (p. 188)
- Chapter 6 The Analysis of Covariance
- 6.1 Introduction (p. 192)
- 6.2 Deriving the formulas for an analysis of covariance from those for a corresponding analysis of variance (p. 199)
- 6.3 An example with one concomitant variable (p. 207)
- 6.4 An example with two concomitant variables (p. 209)
- 6.5 Linear regression on controlled variables subject to error (p. 213)
- Problems (p. 216)
- Part II. The Analysis of Variance in the Case of Other Models
- Chapter 7 Random-Effects Models
- 7.1 Introduction (p. 221)
- 7.2 The one-way layout (p. 221)
- 7.3 Allocation of measurements (p. 236)
- 7.4 The complete two-way layout (p. 238)
- 7.5 The complete three- and higher-way layouts (p. 245)
- 7.6 A nested design (p. 248)
- Problems (p. 258)
- Chapter 8 Mixed Models
- 8.1 A mixed model for the two-way layout (p. 261)
- 8.2 Mixed models for higher-way layouts (p. 274)
- Problems (p. 289)
- Chapter 9 Randomization Models
- 9.1 Randomized blocks: estimation (p. 291)
- 9.2 Latin squares: estimation (p. 304)
- 9.3 Permutation tests (p. 313)
- Problems (p. 329)
- Chapter 10 The Effects of Departures from the Underlying Assumptions
- 10.1 Introduction (p. 331)
- 10.2 Some elementary calculations of the effects of departures (p. 334)
- 10.3 More on the effects of nonnormality (p. 345)
- 10.4 More on the effects of inequality of variance (p. 351)
- 10.5 More on the effects of statistical dependence (p. 359)
- 10.6 Conclusions (p. 360)
- 10.7 Transformations of the observations (p. 364)
- Problems (p. 368)
- Appendices
- I Vector algebra (p. 371)
- Problems (p. 385)
- II Matrix algebra (p. 387)
- Problems (p. 401)
- III Ellipsoids and their planes of support (p. 406)
- Problems (p. 410)
- IV Noncentral X[superscript 2], F, and t (p. 412)
- Problems (p. 415)
- V The multivariate normal distribution (p. 416)
- Problems (p. 418)
- VI Cochran's theorem (p. 419)
- Problems (p. 423)
- F-Tables (p. 424)
- Studentized Range Tables (p. 434)
- Pearson and Hartley Charts for the Power of the F-Test (p. 438)
- Fox Charts for the Power of the F-Test (p. 446)
- Author Index and Bibliography (p. 457)
- Subject Index (p. 467)
Author notes provided by Syndetics
Henry Scheffé was an American statistician. He is known for the Lehmann-Scheffé theorem and Scheffé's method.
There are no comments for this item.