nonparametric tests complete data von bagdonavicius vilijandas (7 Ergebnisse)

Hardcover. Zustand: new. Hardcover. Statistical analysis of data sets usually involves construction of a statistical model of the distribution of data within the available sample and by extension the distribution of all data of the same category in the world. Statistical models are either parametric or non-parametric this distinction is based on whether or not the model can be described in terms of a finite-dimensional parameter and the models must be tested to ascertain whether or not they conform to the data, or are accurate. This book addresses the testing of hypotheses in non-parametric models in the general case for complete data samples. Classical non-parametric tests (goodness-of-fit, homogeneity, randomness, independence) of complete data are considered, and explained. Tests featured include the chi-squared and modified chi-squared tests, rank and homogeneity tests, and most of the test results are proved, with real applications illustrated using examples. The incorrect use of many tests, and their application using commonly deployed statistical software is highlighted and discussed. This book concerns testing hypotheses in non-parametric models. Classical non-parametric tests (goodness-of-fit, homogeneity, randomness, independence) of complete data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Zustand: New. This book concerns testing hypotheses in non-parametric models. Classical non-parametric tests (goodness-of-fit, homogeneity, randomness, independence) of complete data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. Num Pages: 326 pages. BIC Classification: PBK. Category: (P) Professional & Vocational. Dimension: 234 x 163 x 25. Weight in Grams: 628. . 2011. 1st Edition. Hardcover. . . . .

Zustand: New. This book concerns testing hypotheses in non-parametric models. Classical non-parametric tests (goodness-of-fit, homogeneity, randomness, independence) of complete data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. Num Pages: 326 pages. BIC Classification: PBK. Category: (P) Professional & Vocational. Dimension: 234 x 163 x 25. Weight in Grams: 628. . 2011. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Neuware - This book concerns testing hypotheses in non-parametric models.Classical non-parametric tests (goodness-of-fit, homogeneity,randomness, independence) of complete data are considered. Most ofthe test results are proved and real applications are illustratedusing examples. Theories and exercises are provided. The incorrectuse of many tests applying most statistical software is highlightedand discussed.

Hardcover. Zustand: new. Hardcover. Statistical analysis of data sets usually involves construction of a statistical model of the distribution of data within the available sample and by extension the distribution of all data of the same category in the world. Statistical models are either parametric or non-parametric this distinction is based on whether or not the model can be described in terms of a finite-dimensional parameter and the models must be tested to ascertain whether or not they conform to the data, or are accurate. This book addresses the testing of hypotheses in non-parametric models in the general case for complete data samples. Classical non-parametric tests (goodness-of-fit, homogeneity, randomness, independence) of complete data are considered, and explained. Tests featured include the chi-squared and modified chi-squared tests, rank and homogeneity tests, and most of the test results are proved, with real applications illustrated using examples. The incorrect use of many tests, and their application using commonly deployed statistical software is highlighted and discussed. This book concerns testing hypotheses in non-parametric models. Classical non-parametric tests (goodness-of-fit, homogeneity, randomness, independence) of complete data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.