statistica实验设计.pdf

3 Example 3: Analyzing a 3 Full Factorial Box and Draper (1987, page 205) report a study of the behavior of worsted yarn under cycles of repeated loading. (The study was originally conducted by A. Barella and A. Sust for the Technical Committee of the International Wool Textile Organization.) The dependent variable of interest is the number of cycles to failure. Because of large variability in that variable, the log10 transformed dependent variable values were also considered. The data are contained in the data file Textile2.sta. Open this data file by selecting Open from the File menu; most likely, it will be installed in the /Examples/Datasets directory of STATISTICA. Shown below is a partial listing of the data file. The three factors included in the study were: Factor Low Med High 1.Length of specimen (mm) 250 300 350 2.Amplitude of load cycle (mm) 8 9 10 3.Load (g) 40 45 50 In this example, we will first analyze the untransformed dependent variable values, to see how the diagnostic plots available in the Experimental Design module allow you to detect the need for transforming the dependent variable values. In general, the purpose of running experiments with factors at more than 2 levels is to be able to detect non-linearity in the relationships between the factors and the dependent variable of interest. Thus, we will test in this example whether a non-linear model is necessary to explain the dependent variable values. Specifying the Design. To begin the analysis, select Experimental Design (DOE) from the Statistics - Industrial Statistics Six Sigma menu to display the Design Analysis of Experiments Startup Panel. Then select 3(k-p) and Box-Behnken designs and click the OK button. On the Design Analysis of Experiments with Tree-Level