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008			190423t20192019enka b 001 0 eng d
010			_a 2019940509
020			_a0128112166 _qpaperback
020			_a9780128112168 _qpaperback
035			_a(OCoLC)on1020031407
040			_aYDX _beng _cYDX _erda _dBDX _dOCLCQ _dSINLB _dOCLCF _dYDXIT _dPHCSB _dV5E _dOL$ _dDGW _dOCL _dDLC _dBD-ChPU
042			_alccopycat
080			_a519.254:070.3 _bF38
082	0	4	_a650.01/51 F273d 2019 _223
100	1		_aFávero, Luiz Paulo, _eauthor.
245	1	0	_aData science for business and decision making / _cLuiz Paulo Fávero, Patrícia Belfiore.
250			_aFirst edition
260			_aLondon : _bAcademic Press is an imprint of Elsevier, _c2019.
300			_a1227 pages : _billustrations ; _c28 cm.
504			_aIncludes bibliographical references (pages 1195-1214) and index.
505	0		_aPart 1: Foundations of Business Data Analysis -- 1. Introduction to Data Analysis and Decision Making -- 2. Type of Variables and Mensuration Scales -- Part 2: Descriptive Statistics -- 3. Univariate Descriptive Statistics -- 4. Bivariate Descriptive Statistics -- Part 3: Probabilistic Statistics -- 5. Introduction of Probability -- 6. Random Variables and Probability Distributions -- Part 4: Statistical Inference -- 7. Sampling -- 8. Estimation -- 9. Hypothesis Tests -- 10. Non-parametric Tests -- Part 5: Multivariate Exploratory Data Analysis -- 11. Cluster Analysis -- 12. Principal Components Analysis and Factorial Analysis -- Part 6: Generalized Linear Models -- 13. Simple and Multiple Regression Models -- 14. Binary and Multinomial Logistics Regression Models -- 15. Regression Models for Count Data: Poisson and Negative Binomial -- Part 7: Optimization Models and Simulation -- 16. Introduction to Optimization Models: Business Problems Formulations and Modeling -- 17. Solution of Linear Programming Problems -- 18. Network Programming -- 19. Integer Programming -- 20. Simulation and Risk Analysis Part 8: Other Topics -- 21. Design and Experimental Analysis -- 22. Statistical Process Control -- 23. Data Mining and Multilevel Modeling.
520			_aData Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®.
526			_aBBA, MBA, EMBA.
650		0	_aCommercial statistics. _95195
650		0	_aDecision making _xStatistical methods.
650		7	_aDecision making _xStatistical methods. _2fast
650		7	_aCommercial statistics. _2fast _95195
700	1		_aBelfiore, Patrícia Prado, _eauthor.
776	0	8	_iElectronic version: _aFávero, Luiz Paulo. _tData science for business and decision making. _dLondon, United Kingdom : Academic Press, an imprint of Elsevier, 2019 _w(OCoLC)1098238973
906			_a0 _bibc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_2ddc _cBK
999			_c6309 _d6309