By Joaquim P. Marques de Sá
Meant for an individual desiring to use statistical research to a wide number of technological know-how and engineering difficulties, this ebook exhibits the best way to use SPSS, MATLAB, STATISTICA and R for facts description, statistical inference, type and regression, issue research, survival facts and directional facts. The 2d version contains the R language, a brand new part on bootstrap estimation tools and a better therapy of tree classifiers, plus extra examples and workouts.
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Extra info for Applied statistics: using SPSS, STATISTICA, MATLAB and R
Example text
6, even with 25-sized samples one would often be wrong when admitting that a data distribution is normal because a statistical test didn’t reject that possibility at a 95% confidence level. More: one would often be accepting the normality of data generated with asymmetrical and even bimodal distributions! Data distribution modelling is a difficult problem that usually requires large samples and even so one must bear in mind that most of the times and beyond a reasonable doubt one only has evidence of a model; the true distribution remains unknown.
Notice the fields for filling in variable labels and missing data codes. 2 STATISTICA Data Entry With STATISTICA one starts by creating a new data file (File, New) with the desired number of variables and cases, before pasting or typing in the data. There is also the possibility of using any previous template data file and adjusting the number of variables and cases (click the right button of the mouse over the variable column(s) or case row(s) or, alternatively, use Insert). One may proceed to define the variables, by assigning them a specific name and declaring their type.
This site explains the R history and indicates a set of URLs (the so-called CRAN mirrors) that can be used for downloading R. It also explains the relation of the R programming language to other statistical processing languages such as S and S-Plus. Performing statistical analysis with MATLAB and R gives the user complete freedom to implement specific algorithms and perform complex custom-tailored operations. MATLAB and R are also especially useful when the statistical operations are part of a larger project.