Principles and Procedures of Statistics: A Biometrical Approach
Observations; 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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accept additional adjusted alternative analysis of variance apply appropriate approximation associated average binomial block called coefficient column common comparisons complete Compute confidence interval correlation covariance criterion defined degrees of freedom dependent determine differences discussed distribution effects equal equation error estimate example Exercise expected experiment experimental factor fixed frequency give given homogeneity illustration increase independent individual interaction involved linear matrix mean square measure method missing multiple normal Note null hypothesis observations obtained pairs parameters percent plants plot population population mean possible present probability problem procedure random ratio referred regression relation Repeat replication response sample means significant single Source standard deviation statistics sum of squares Table term tion transformation treatment means true Type units values variable variation weight yield zero