MESSY EXPERIMENTAL DESIGNS

This paper describes the statistical analysis of an agricultural experiment that was conducted in a very complex, but somewhat reasonable, experimental design. A correct analysis of data collected from the experimental design used requires the estimation of 8 error terms

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https://ecommons.udayton.edu/roesch_symposium_content/1036/thumbnail.jp

ON MULTIVARIATE ANALYSES OF CROSSOVER DESIGNS

In crossover experiments, treatments are assigned to experimental units in successive periods. Traditional analyses of crossover designs with three or more periods assume that the observations in successive periods satisfy conditions similar to those utilized in the analysis of many repeated measures experiments. The successive measurements are assumed to satisfy conditions known as the Huynh-Feldt conditions. This paper gives a test for the Huynh-Feldt conditions and discusses possible analyses of crossover experiments, including tests for carryover, when the Huynh-Feldt conditions are not satisfied

ALTERNATIVE ANALYSES OF CROSSOVER DESIGNS WITH MORE THAN TWO PERIODS

A crossover experiment is a special form of a repeated measures experiment. An appropriate analysis of a repeated measures experiment depends on the form of the varian-cecovariance matrix of the repeated measures. Certain forms of this matrix yield valid analysis of variance F -tests while other forms invalidate these tests. In a crossover experiment where analysis of variance tests are invalid, two alternative tests of a linear contrast of the parameters are proposed. In addition to these approximate t-tests, three alternative methods for testing for equal treatment effects and equal carryover effects are proposed. A simulation study is conducted to evaluate these proposed alternative test procedures for power. Confidence levels and confidence interval lengths are also examined for those procedures from which an estimate of the linear contrast can be made

COMPARING ANALYSES OF UNBALANCED SPLIT-PLOT EXPERIMENTS

Several procedures for constructing confidence intervals and testing hypotheses about fixed effects in unbalanced split-plot experiments have previously been presented and discussed by Remmenga and Johnson. They recommended a few of the procedures they considered as useful and reliable procedures. Since the advent of the SAS® MIXED procedure, mixed model analyses with REML estimates of the variance components are easily accessible to researchers. This paper compares the analysis of unbalanced split-plot experiments using mixed model procedures with REML estimates of the variance components to the previously established procedures by means of additional simulation studies

City Guide: Bar Style

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STATISTICAL ANALYSIS SOFTWARE FOR MULTIPLICATIVE INTERACTION MODELS

In a two-way cross-classified experiment, one is almost always interested in whether the two factors interact or not. When there are no independent replications, there are no traditional tests for interaction. This research considers the problem of analyzing a two-way cross-classified experiment using multiplicative interaction models when there are no independent replications and interaction between the two factors may exist. The purpose of this research is to develop SAS® macros to provide user-friendly statistical software for the analysis of interaction in two-way experiments. The macros also provide many useful graphical displays including displays to help one determine the pattern of interaction when a pattern exists and to help one interpret the results of the analyses

SOME EXPERIENCES WITH NEURAL NETWORKS

This paper gives a brief overview of artificial neural networks which may be used to model data similar to the kind where one usually considers regression models. Many practitioners believe that neural networks perform better than regression models for prediction purposes. Some simulations were performed using three different neural net programs, namely Braincel, Ripley\u27s S+ program, and Nychka\u27s S+ program. These simulations reveal some interesting aspects of neural net programs which should be of interest to anyone considering the use of neural net programs to model continuous data

OPTIONS FOR ANALYZING UNBALANCED SPLIT-PLOT EXPERIMENTS: A CASE STUDY

Unbalanced split-plot experiments present many analysis problems. This paper discusses some of the difficulties by comparing the results of the analysis recommended by Milliken and Johnson (1984) to a set of minimal sufficient statistics using a small experiment from Milliken and Johnson as a case study. The estimators used by Milliken and Johnson are not necessarily the best (smallest variance) estimators. A set of minimal sufficient statistics is used to show that the whole plot error term suggested by Milliken and Johnson does not have a distribution that is proportional to an exact chi-square distribution and is not always independent of parameter function estimators. Other options for analyzing unbalanced split-plot experiments and unbalanced repeated measures experiments in which the repeated measures satisfy the Huyhn-Feldt (1970) conditions are proposed