Principles and Procedures of Statistics: A Biometrical Approach
This book provides a thorough treatment of major statistical methods and techniques for those requiring a strong foundation in applied statistics. The basic ideas of inference from data, the principle of fitting models by least squares, and careful interpretation of results are stressed to provide a firm grounding in both principles and procedures. This edition includes modern topics, computer output and analysis, as well as updated real-world data sets. Moreover, there is an extensive chapter on the principles of experimental design.
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Sampling from a Normal Distribution
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95 percent confidence adjusted alternative analysis of variance appropriate approximation assumption binomial distribution chi-square column comparisons completely random design components compute confidence interval contrast correlation covariance critical value cultivar data of Exercise data of Table degrees of freedom Dependent variable difference equation error mean square error rate estimate of a2 example expected value experimental error experimental units experimentwise error rate F test F value Pr>F factor given gives homogeneity interaction intercept Latin square LSMEAN main effects matrix measured multiple normal distribution null hypothesis number of observations obtained orthogonal Output P-value pairs percent confidence interval plants plot population mean pots probability procedure random sample randomized complete block ratio Repeat Exercise replication residuals sample means significant simple effects Source df square F value standard deviation standard error statistics sum of squares test criterion Test the null treatment means true Type vector weight zero