Analysis of messy data by George A. Milliken, Dallas E. Johnson

By George A. Milliken, Dallas E. Johnson

A best-selling reference for 17 years, research of Messy info: quantity 1 has now been largely revised and taken completely brand new. The authors have streamlined the presentation and integrated a few fresh advancements within the box, together with advances in random results versions and refinements to a number of comparability approaches. most significantly, they've got thoroughly up-to-date fabric concerning software program and element how SAS-Mixed, SAS-GLM, and different applications can be utilized to enhance scan layout and version research. effortless to learn with strong examples and a comfy structure, this version will definitely take its position as a favourite reference of scan designers and statisticians.

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5 Inferences on Linear Combinations The problems of testing hypotheses about and constructing confidence intervals for an t arbitrary linear combination of the treatment means, Âi=1 ci mi, are discussed in this section 2 when the variances s i are too unequal to apply the tests and confidence intervals discussed in Chapter 1. 01 or 1% level. If there is not sufficient evidence to believe that the variances are unequal, then one can use the results in Chapter 1 to make inferences about the treatment means.

Let r S= Ân k =1 nk I[n k > 2] k -2 where I[nk > 2] is an indicator function with the value of 1 when nk > 2 and 0 otherwise. The approximate denominator degrees of freedom for the distribution of Fc are Ï 2S Ô n = ÌS - r Ô0 Ó if S > r if S £ r The above process can be used to provide a test of the equal means hypothesis by selecting a set of t - 1 linearly independent contrasts of the mi. The SAS-Mixed procedure implements a version of this approximation to the denominator degrees of freedom associated with an approximate F statistic with multiple degrees of freedom in the numerator.

Three birds did not survive the study, thus producing missing data. Lung Volumes for Birds Raised at Different Simulated Elevations Elevation 1000 ft Elevation 2000 ft Elevation 3000 ft Elevation 4000 ft Bird Volume Bird Volume Bird Volume Bird Volume Bird Volume 156 151 161 152 164 153 163 8 9 12 13 14 160 160 154 152 153 15 16 18 19 20 21 156 173 165 172 169 168 22 23 24 25 26 27 28 168 167 171 173 167 167 173 29 30 31 32 33 34 35 177 170 169 176 183 178 174 1 2 3 4 5 6 7 Elevation 5000 ft 1) Write down a model appropriate to describe the data.

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