000 02259 a2200265 4500
999 _c10044
_d10044
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008 190218b xxu||||| |||| 00| 0 eng d
020 _a9781439855676
_qhbk.
040 _cSPAB
041 _aeng
082 _a512.6202855133
_bBIL-A
100 _aBilder, Christopher R.
_915558
245 _aAnalysis of categorical data with R.
_cChristopher R. Bilder and Thomas M. Loughin
260 _bCRC Press,
_c2015
_aBoca Raton
300 _axii, 533p.
440 _aTexts in Statistical Science.
_915559
505 _a1. Analyzing a binary response, part 1: introduction -- 2. Analyzing a binary response, part 2: regression models -- 3. Analyzing a multicategory response -- 4. Analyzing a count response -- 5. Model selection and evaluation -- 6. Additional topics -- A. An introduction to R -- B. Likelihood methods.
520 _aWe live in a categorical world! From a positive or negative disease diagnosis to choosing all items that apply in a survey, outcomes are frequently organized into categories so that people can more easily make sense of them. However, analyzing data from categorical responses requires specialized techniques beyond those learned in a first or second course in Statistics. We use R not only as a data analysis method but also as a learning tool. For example, we use data simulation to help readers understand the underlying assumptions of a procedure and then to evaluate that procedure's performance. We also provide numerous graphical demonstrations of the features and properties of various analysis methods. The focus of this book is on the analysis of data, rather than on the mathematical development of methods. We o er numerous examples from a wide rage of disciplines medicine, psychology, sports, ecology, and others and provide extensive R code and output as we work through the examples. We give detailed advice and guidelines regarding which procedures to use and why to use them. While we treat likelihood methods as a tool, they are not used blindly.
650 _aAR/PL
_911385
690 _aR (Computer program language).
_915525
690 _aCategories (Mathematics)-Data processing.
_915560
700 _aLoughin, Thomas M.
_915561
942 _2ddc
_cTXT