Principles and Procedures of Statistics: A Biometrical ApproachObservations; Probability; Sampling from a normal distribution; Comparisons involving two sample means; Principles of experimental design; Analysis of variance I: the one-way classification; Multiple comparisons; Analysis of variance II: multiway classifications; Linear regression; Linear correlation; Matrix notation; Linear regression in matrix notation; Multiple and partial regression and correlation; Analysis of variance III: factorial experiments; Analysis of variance IV: split-plot designs and analysis; Analysis of covariance; Analysis of variance V: unequal subclass numbers; Curve fitting; Some uses of Chi square; Enumeration Data I: one-way classifications; Enumeration Data II: contingency tables; Some discrete distributions; Nonparametric statistics; Sampling finite populations. |
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alternative analysis of variance apply appropriate b₁ b₂ binomial distribution Chap column comparisons complete block design completely random design components Compute confidence interval covariance data of Exercise data of Table degrees of freedom differences error mean square error rate estimate of o² example experiment experimental error experimental units factor frequency H₁ homogeneity interaction Latin square linear matrix measure ment multiple n₁ n₂ normal distribution null hypothesis number of observations number of replicates obtained orthogonal pairs parameters plants plot population mean pots probability procedure random sample randomized complete block ratio regression coefficient regression line Repeat Exercise residual sample means sampling error significant sources of variation standard deviation statistics sum of squares test criterion Test the null tion transformation treatment sum treatment totals Type I error weight X₁ Y₁ Y₂ zero Σ Υ σ² ΣΥ