By Gary W. Oehlert
• while to exploit quite a few designs
• find out how to examine the results
• tips to realize numerous layout options
Also, not like different older texts, the booklet is totally orientated towards using statistical software program in studying experiments.
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Additional resources for A First Course in Design and Analysis of Experiments
Thus we have a completely randomized design, with five treatment groups and each ni fixed at 48. The seedlings were the experimental units, and plant dry weight was the response. This is a nice, straightforward experiment, but let’s look over the steps in planning the experiment and see where some of the choices and compromises were made. It was suspected that damage might vary by pH level, plant developmental stage, and plant species, among other things. This particular experiment only addresses pH level (other experiments were conducted separately).
The word random is quoted above because these numbers are not truly random. The numbers in the table are the same every time you read it; they don’t change unpredictably when you open the book. The numbers produced by the software package are from an algorithm; if you know the algorithm you can predict the numbers perfectly. They are technically pseudorandom numbers; that is, numbers that possess many of the attributes of random numbers so that they appear to be random and can usually be used in place of random numbers.
The beauty of randomization is that it helps prevent confounding, even for factors that we do not know are important. Randomization balances the population on average 16 Randomization and Design Here is another example of randomization. A company is evaluating two different word processing packages for use by its clerical staff. Part of the evaluation is how quickly a test document can be entered correctly using the two programs. We have 20 test secretaries, and each secretary will enter the document twice, using each program once.