By Joaquim P. Marques de Sá
This sensible reference presents a complete creation and educational at the major statistical research themes, demonstrating their answer with the commonest software program package deal. meant for an individual desiring to use statistical research to a wide number of technological know-how and enigineering difficulties, the publication explains and exhibits the best way to use SPSS, MATLAB, STATISTICA and R for research corresponding to information description, statistical inference, class and regression, issue research, survival info and directional data. It concisely explains key techniques and techniques, illustrated via sensible examples utilizing actual information, and incorporates a CD-ROM with software program instruments and knowledge units. Readers research which software program instruments to use and achieve insights into the comparative features of the first software program programs. significant advancements of the second one version are the inclusion of the R language, a brand new part on bootstrap estimation tools and a much better remedy of tree classifiers in addition to additional examples and exercises.
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Extra info for Applied Statistics Using SPSS, STATISTICA, MATLAB and R
A compromise value achieving a useful tolerance with an affordable sample size has to be found. 6 Statistical Significance and Other Significances Statistics is surely a recognised and powerful data analysis tool. Because of its recognised power and its pervasive influence in science and human affairs people tend to look to statistics as some sort of recipe book, from where one can pick up a recipe for the problem at hand. Things get worse when using statistical software and particularly in inferential data analysis.
In the present book we concentrate on how to choose adequate analytical methodologies and give precise interpretation of the results. Besides theoretical explanations and words of caution the book includes a large number of examples that in our opinion help to solidify the notions of adequacy and of precise interpretation of the data and the results. The other two sources of misuse − flawed data and lack of knowledge of the subject matter – are the responsibility of the practitioner. In what concerns statistical inference the reader must exert extra care of not applying statistical methods in a mechanical and mindless way, taking or using the software results uncritically.
Research literature is full with examples of improperly validated models that are later on dropped out when more data becomes available and the initial optimism plunges down. The love-at-first-sight is even stronger when using computer software that automatically searches for the best set of variables describing the model. The book of Chamont Wang (Wang C, 1993), where many illustrations and words of caution on the topic of inferential statistics can be found, mentions an experiment where 51 data samples were generated with 100 random numbers each and a regression model was searched for “explaining” one of the data samples (playing the role of dependent variable) as a function of the other ones (playing the role of independent variables).