Abstract
The objectives of preliminary data analysis are to edit the data to prepare it for further analysis, describe the key features of the data, and summarize the results. This chapter deals with quantitative and qualitative approaches to achieving these objectives. Topics covered include scales of measurement, types of data, graphical methods of analysisᾢincluding histograms, probability plots, and other graphical representations of data, and basic descriptive statisticsᾢmean, median, fractiles, standard deviation, and so forth. The chapter concludes with a discussion of the use of probability plots in preliminary model selection.
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Notes
- 1.
Some other software packages, including Splus (http://www.insightful.com) and R-language (http://cran.r-project.org/) are also used in later Chapters.
- 2.
In a very real sense, probability and statistics are inverses of one another. Probability deals with models of randomness that can be used to make statements about the kinds of data that may occur. Statistics deals with the use of data to make statements about the model.
- 3.
Related terms are percentile and quantile.
- 4.
The exception occurs if the CDF is constant over some interval and increasing on either side of the interval.
- 5.
Minitab removes smallest and largest 5% (using the nearest integer to .05n). This usually removes the values causing the distortion and provides a more meaningful measure. Other (less drastic) methods of dealing with outliers will be discussed in Chap. 9.
- 6.
The subscript s is for Charles Spearman, who devised the measure in 1904.
- 7.
The steps may vary with respect to the version of the Minitab software.
- 8.
As noted by a well-known statistician, Oscar Kempthorne, “No model is correct. But some are useful!”.
- 9.
In fact, the “goodness-of-fit” statistic is given as AD* = 0.436, which indicates a relatively good fit. This will be discussed further in Chap. 10.
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Blischke, W.R., Rezaul Karim, M., Prabhakar Murthy, D.N. (2011). Preliminary Data Analysis. In: Warranty Data Collection and Analysis. Springer Series in Reliability Engineering. Springer, London. https://doi.org/10.1007/978-0-85729-647-4_8
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