- Häftad (Paperback)
- 4 ed
- Cengage Learning EMEA
- 252 x 194 x 21 mm
- 1062 g
Du kanske gillar
Statistics for Business and Economics
Fri frakt inom Sverige för privatpersoner.
Fler böcker av författarna
Bloggat om Statistics for Business and Economics
Jim Freeman is Senior Lecturer in Statistics and Operational Research at Manchester Business School, United Kingdom. He has taught undergraduate and postgraduate courses in business statistics and operational research courses to students from a wide range of management and engineering backgrounds. For many years he was also responsible for providing introductory statistics courses to staff and research students at the University of Manchester's Staff Teaching Workshop. Through his gaming and simulation interests he has been involved in a significant number of external consultancy and grant-aided projects. More recently he received significant government ('KTP') funding for research in the area of risk management. In July 2008 he was Editor of the Operational Research Society's OR Insight journal. In November 2012 he received the Outstanding Achievement Award at the Decision Sciences Institute 43rd Annual Meeting in San Francisco. Eddie Shoesmith was formerly Senior Lecturer in Statistics and Programme Director for undergraduate business and management programmes in the School of Business, University of Buckingham, UK. At Buckingham, before joining the School of Business, he held posts as Dean of Sciences and Head of Psychology. He has taught introductory and intermediate-level applied statistics courses to undergraduate and postgraduate student groups in a wide range of disciplines: business and management, economics, accounting, psychology, biology and social sciences. He has also taught statistics to social and political sciences undergraduates at the University of Cambridge. Dennis J. Sweeney is Professor of Quantitative Analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. He has published more than 30 articles in the area of management science and statistics. The National Science Foundation, IBM, Procter & Gamble, Federated Department Stores, Kroger and Cincinnati Gas & Electric have funded his research, which has been published in Management Science, Operations Research, Mathematical Programming, Decision Sciences and other journals. Professor Sweeney has co-authored ten textbooks in the areas of statistics, management science, linear programming and production and operations management. David R. Anderson is Professor of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. Born in Grand Forks, North Dakota, he earned his BS, MS and PhD degrees from Purdue University. Professor Anderson has served as Head of the Department of Quantitative Analysis and Operations Management and as Associate Dean of the College of Business Administration. In addition, he was the coordinator of the college's first executive programme. Thomas A. Williams is Professor of Management Science in the College of Business at Rochester Institute of Technology (RIT). Professor Williams is the co-author of 11 textbooks in the areas of management science, statistics, production and operations management and mathematics. He has been a consultant for numerous Fortune 500 companies in areas ranging from the use of elementary data analysis to the development of large-scale regression models.
1.Data and Statistics 2.Descriptive Statistics: Tabular and Graphical Presentations 3.Descriptive Statistics: Numerical Measures 4.Introduction to Probability 5.Discrete Probability Distributions 6.Continuous Probability Distributions 7.Sampling and Sampling Distributions 8.Interval Estimation 9.Hypothesis Tests 10.Statistical Inference about Means and Proportions with Two Populations 11.Inferences about Population Variances 12.Tests of Goodness of Fit and Independence 13.Experimental Design and Analysis of Variance 14.Simple Linear Regression 15.Multiple Regression 16.Regression Analysis: Model Building 17.Time Series Analysis and Forecasting 18.Non-Parametric Methods Appendix A Appendix B Glossary Index