Statistical methods for business and economics - Nieuwenhuis, Gert Lukas (Boston : McGraw-Hill, 2009)
This brand new book in statistics aims to provide an introduction to the key methods and techniques essential to a typical statistics syllabus, whilst also helping students to develop the skills needed to analyze, interpret and prepare data for use in business, economics and related disciplines. Covering the essential methods required at undergraduate level, the book is structured into four parts that deal with descriptive statistics, probability, sample theory and inferential statistics, taking students from the basics through to more advanced topics such as multiple linear regression. Every chapter contains clear descriptions of each technique, illustrated with numerous worked examples to aid students in understanding how to practice statistical methods.The real data used in the examples is drawn from European sources. The text also contains longer case examples set in a European business context, to show how statistics is used everyday in the business environment. Finally, each chapter concludes with a variety of exercises to test students' ability to apply the theory and attain a high level of competence in using statistics. This comprehensive book is ideal for student of statistics at undergraduate level taking an introductory module in the topic.
Preface Guided Tour Technology to enhance teaching and learning Introduction and basic concepts Descriptive Statistics Tables and graphs Measures of location Measures of variation Pairs of variables Probability Definitions of Probability Calculation of probabilities Probability distribution, expectation, variance Families of discrete distributions Families of continuous distributions Joint probability distributions Sampling theory Random samples The sample mean Sample proportion and other sample statistics Inferential statistics Interval estimation and hypothesis testing: a general introduction Confidence intervals and tests for and Statistical inference about Confidence intervals and tests to compare two parameters Simple linear regression Multiple linear regression: introduction Multiple linear regression: extension Multiple linear regression: model violations Time series and forecasting Chi-squared tests Nonparametric statistics Excel and SPSS (on Internet) Summation operator Greek letters Tables Numeric answers of exercises
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Keywords: Statistics