Statisitics / James T. McClave, Terry Sincich
Material type:
- 0135820103
- 22nd ed. 519.5 MCC
Item type | Current library | Collection | Call number | Status | Barcode | |
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CUoM Library General Stacks | Mathematics/Physics/Chemistry/Biology | 519.5 MCC (Browse shelf(Opens below)) | Available | 00005049 | |
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CUoM Library General Stacks | Mathematics/Physics/Chemistry/Biology | 519.5 MCC (Browse shelf(Opens below)) | Available | 00005047 | |
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CUoM Library General Stacks | Mathematics/Physics/Chemistry/Biology | 519.5 MCC (Browse shelf(Opens below)) | Available | 00005048 |
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519.5 MCC Statistics / | 519.5 MCC Statisitics / | 519.5 MCC Statisitics / | 519.5 MCC Statisitics / | 519.5 MCC Statistics for business and economics / | 519.5 MED A brief course in business statistics / | 519.5 MEN Introduction to probability and statistics / |
Includes index
This custom for the university of Missouri-Columbia edition of Statistics is an introductory text emphasizing inference, with extensive coverage of data collection and analysis as needed to evaluate the reported results of statistical studies and make good decisions. As in earlier editions, the text stresses the development of statistical thinking, the assessment of credibility, and the value of the inferences made from data, both by those who consume and those who produce them. It assumes a mathematical background of basic algebra. The text incorporates the following features, developed from the American Statistical Association's (ASA) Guidelines for Assessment and Instruction in Statistics Education (GAISE) Project: Emphasize statistical literacy and develop statistical thinking Use real data in applications Use technology for developing conceptual understanding and analyzing data Foster active learning in the classroom Stress conceptual understanding rather than mere knowledge of procedures Emphasize intuitive concepts of probability A briefer version of the book, A First Course in Statistics, is available for single-semester courses that include minimum coverage of regression analysis, analysis of variance, and categorical data analysis
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