By Gary W. Oehlert
• whilst to exploit quite a few designs
• the best way to examine the results
• find out how to realize a variety of layout options
Also, not like different older texts, the booklet is absolutely orientated towards using statistical software program in interpreting experiments.
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Additional resources for A First Course in Design and Analysis of Experiments
We might assume that all the treatments have the same mean response, or that each treatment has its own mean, or that the means in the treatments are a straight line function of the treatment pH. Each one of these models for the means has its own parameters, namely the common mean, the five separate treatment means, and the slope and intercept of the linear relationship, respectively. The second basic part of our data models is a description of how the data vary around the treatment means. This is the “model for the errors” or “structure for the errors”.
24 In fact, randomization itself can be used as a basis for inference. The advantage of this randomization approach is that it relies only on the randomization that we performed. It does not need independence, normality, and the other assumptions that go with linear models. The disadvantage of the randomization approach is that it can be difficult to implement, even in relatively small problems, though computers make it much easier. Furthermore, the inference that randomization provides is often indistinguishable from that of standard techniques such as ANOVA.
Does the second program have an advantage because the secretary will be familiar with the document and thus enter it faster? Or maybe the second program will be at a disadvantage because the secretary will be tired and thus slower. Two randomized designs that could be considered are: 1. For each secretary, toss a coin: the secretary will use the programs in the orders AB and BA according to whether the coin is a head or a tail, respectively. 2. Choose 10 secretaries at random for the AB order, the rest get the BA order.