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Like most academic authors, my views are a joint product of my teaching and my research. Needless to say, my views reflect the biases that I have acquired. One way to articulate the rationale (and limitations) of my biases is through the preface of a truly great text of a previous era, Cooley and Lohnes (1971, p. v). They draw a distinction between mathematical statisticians whose intel- lect gave birth to the field of multivariate analysis, such as Hotelling, Bartlett, and Wilks, and those who chose to concentrate much of their attention on methods of analyzing data in the sciences and of interpreting the results of statistical analysis . . . . (and) . . . who are more interested in the sciences than in mathematics, among other characteristics. I find the distinction between individuals who are temperamentally mathe- maticians (whom philosophy students might call Platonists) and scientists (Aristotelians) useful as long as it is not pushed to the point where one assumes mathematicians completely disdain data and scientists are never interested in contributing to the mathematical foundations of their discipline. I certainly feel more comfortable attempting to contribute in the scientist rather than the mathematician role. As a consequence, this book is primarily written for individuals concerned with data analysis. However, as noted in Chapter 1, true expertise demands familiarity with both traditions.
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Like most academic authors, my views are a joint product of my teaching and my research. Needless to say, my views reflect the biases that I have acquired. One way to articulate the rationale (and limitations) of my biases is through the preface of a truly great text of a previous era, Cooley and Lohnes (1971, p. v). They draw a distinction between mathematical statisticians whose intel- lect gave birth to the field of multivariate analysis, such as Hotelling, Bartlett, and Wilks, and those who chose to concentrate much of their attention on methods of analyzing data in the sciences and of interpreting the results of statistical analysis . . . . (and) . . . who are more interested in the sciences than in mathematics, among other characteristics. I find the distinction between individuals who are temperamentally mathe- maticians (whom philosophy students might call Platonists) and scientists (Aristotelians) useful as long as it is not pushed to the point where one assumes mathematicians completely disdain data and scientists are never interested in contributing to the mathematical foundations of their discipline. I certainly feel more comfortable attempting to contribute in the scientist rather than the mathematician role. As a consequence, this book is primarily written for individuals concerned with data analysis. However, as noted in Chapter 1, true expertise demands familiarity with both traditions.
Reviews