Applied Statistics Using SPSS, STATISTICA, MATLAB and R
Contributor(s): Marques de S�, Joaquim P [editor.2 ] | SpringerLink (Online service)0.
Material type:
BookBerlin, Heidelberg : Springer Berlin Heidelberg, 2007. Description: XXIV, 505 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540719724.Subject(s): Statistics | Computer software | Probabilities | Applied mathematics | Engineering mathematics | Computational intelligence | Complexity, Computational.1 | Statistics.2 | Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.2 | Probability Theory and Stochastic Processes.2 | Mathematical Software.2 | Computational Intelligence.2 | Complexity.2 | Appl.Mathematics/Computational Methods of Engineering.1DDC classification: 519.5 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
| PK Kelkar Library, IIT Kanpur | Available | EBK9350 |
Presenting and Summarising the Data -- Estimating Data Parameters -- Parametric Tests of Hypotheses -- Non-Parametric Tests of Hypotheses -- Statistical Classification -- Data Regression -- Data Structure Analysis -- Survival Analysis -- Directional Data.
This successful textbook is intended for students, professionals and research workers who need to apply statistical analysis to a large variety of practical problems using SPSS, MATLAB, STATISTICA and R. The book provides a comprehensive coverage of the main statistical analysis topics important for practical applications such as data description, statistical inference, classification and regression, factor analysis, survival data and directional statistics. The relevant notions and methods are explained concisely, illustrated with practical examples using real data, presented with the distinct intention of clarifying sensible practical issues. The solutions presented in the examples are obtained with one of the software packages in a pedagogical way. It provides guidance on how to use SPSS, MATLAB, STATISTICA and R in statistical analysis applications without having to delve in the manuals. Major improvements of the second edition are the inclusion of the R language as one of the application tools, a new section on bootstrap estimation methods, a revised explanation and treatment of tree classifiers as well as extra examples and exercises.
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