Basic Business Statistics: Concepts and applications (Australasian and Pacific edition)

Description

Authorised adaptation from the United States edition entitled Basic Business Statistics, 13th edition, ISBN 0321870026 by Berenson, Mark L., Levine, David M., Szabat, Kathryn A., published by Pearson Education, Inc. This fifth Australasian and Pacific edition of Basic Business Statistics: Concepts and Applications continues to build on the strengths of the fourth edition, and extends the teaching foundation of the previous American editions. While the teaching philosophy of this text is based upon the principles of the American book, each chapter has been carefully revised to include practical examples and a language and style that is more applicable to Australasian and Pacific readers. Academics are given the flexibility of designing an engaging unit for a mixed cohort of students with courseware that drives technical and soft skills through authentic learning tools and assignments for hybrid, online and face to face units. Additionally, this product Academics are given the flexibility of designing an engaging unit for a mixed cohort of students with courseware that drives technical and soft skills through authentic learning tools and assignments for hybrid, online and face to face units. Additionally, this product • Has respected and updated/current content • Can be used across First Year and Introductory units in all undergraduate Maths or Business courses Front Cover Front Matter Half Title Full Title Imprint Brief Contents Detailed Contents Preface Acknowledgements How to use this book About the authors Part 1 Presenting and describing information Chapter 1 Defining and collecting data 1.1 Basic concepts of data and statistics 1.2 Types of variables 1.3 Collecting data 1.4 Types of survey sampling methods 1.5 Evaluating survey worthiness 1.6 The growth of statistics and information technology Summary Key terms References Chapter review problems Continuing cases Chapter 1 Excel Guide Chapter 2 Organising and visualising data 2.1 Organising and visualising categorical data 2.2 Organising numerical data 2.3 Summarising and visualising numerical data 2.4 Organising and visualising two categorical variables 2.5 Visualising two numerical variables 2.6 Business analytics applications – descriptive analytics 2.7 Misusing graphs and ethical issues Summary Key terms References Chapter review problems Continuing cases Chapter 2 Excel Guide Chapter 3 Numerical descriptive measures 3.1 Measures of central tendency, variation and shape 3.2 Numerical descriptive measures for a population 3.3 Calculating numerical descriptive measures from a frequency distribution 3.4 Five-number summary and box-and-whisker plots 3.5 Covariance and the coefficient of correlation 3.6 Pitfalls in numerical descriptive measures and ethical issues Summary Key formulas Key terms Chapter review problems Continuing cases Chapter 3 Excel Guide End of Part 1 problems Part 2 Measuring uncertainty Chapter 4 Basic probability 4.1 Basic probability concepts 4.2 Conditional probability 4.3 Bayes’ theorem 4.4 Counting rules 4.5 Ethical issues and probability Summary Key formulas Key terms Chapter review problems Continuing cases Chapter 4 Excel Guide Chapter 5 Some important discrete probability distributions 5.1 Probability distribution for a discrete random variable 5.2 Covariance and its application in finance 5.3 Binomial distribution 5.4 Poisson distribution 5.5 Hypergeometric distribution Summary Key formulas Key terms Chapter review problems Chapter 5 Excel Guide Chapter 6 The normal distribution and other continuous distributions 6.1 Continuous probability distributions 6.2 The normal distribution 6.3 Evaluating normality 6.4 The uniform distribution 6.5 The exponential distribution 6.6 The normal approximation to the binomial distribution Summary Key formulas Key terms Chapter review problems Continuing cases Chapter 6 Excel Guide Chapter 7 Sampling distributions 7.1 Sampling distributions 7.2 Sampling distribution of the mean 7.3 Sampling distribution of the proportion Summary Key formulas Key terms References Chapter review problems Continuing cases Chapter 7 Excel Guide End of Part 2 problems Part 3 Drawing conclusions about populations based only on sample information Chapter 8 Confidence interval estimation 8.1 Confidence interval estimation for the mean (σ known) 8.2 Confidence interval estimation for the mean (σ unknown) 8.3 Confidence interval estimation for the proportion 8.4 Determining sample size 8.5 Applications of confidence interval estimation in auditing 8.6 More on confidence interval estimation and ethical issues Summary Key formulas Key terms References Chapter review problems Continuing cases Chapter 8 Excel Guide Chapter 9 Fundamentals of hypothesis testing: One-sample tests 9.1 Hypothesis-testing methodology 9.2 Z test of hypothesis for the mean (σ known) 9.3 One-tail tests 9.4 t test of hypothesis for the mean (σ unknown) 9.5 Z test of hypothesis for the proportion 9.6 The power of a test 9.7 Potential hypothesis-testing pitfalls and ethical issues Summary Key formulas Key terms References Chapter review problems Continuing cases Chapter 9 Excel Guide Chapter 10 Hypothesis testing: Two-sample tests 10.1 Comparing the means of two independent populations 10.2 Comparing the means of two related populations 10.3 F test for the difference between two variances 10.4 Comparing two population proportions Summary Key formulas Key terms References Chapter review problems Continuing cases Chapter 10 Excel Guide Chapter 11 Analysis of variance 11.1 The completely randomised design: One-way analysis of variance 11.2 The randomised block design 11.3 The factorial design: Two-way analysis of variance Summary Key formulas Key terms References Chapter review problems Continuing cases Chapter 11 Excel Guide End of Part 3 problems Part 4 Determining cause and making reliable forecasts Chapter 12 Simple linear regression 12.1 Types of regression models 12.2 Determining the simple linear regression equation 12.3 Measures of variation 12.4 Assumptions 12.5 Residual analysis 12.6 Measuring autocorrelation - The Durbin-Watson statistic 12.7 Inferences about the slope and correlation coefficient 12.8 Estimation of mean values and prediction of individual values 12.9 Pitfalls in regression and ethical issues Summary Key formulas Key terms References Chapter review problems Continuing cases Chapter 12 Excel Guide Chapter 13 Introduction to multiple regression 13.1 Developing the multiple regression model 13.2 R2, adjusted R2 and the overall F test 13.3 Residual analysis for the multiple regression model 13.4 Inferences concerning the population regression coefficients 13.5 Testing portions of the multiple regression model 13.6 Using dummy variables and interaction terms in regression models 13.7 Collinearity Summary Key formulas Key terms References Chapter review problems Continuing cases Chapter 13 Excel Guide Chapter 14 Time-series forecasting and index numbers 14.1 The importance of business forecasting 14.2 Component factors of the classical multiplicative time-series model 14.3 Smoothing the annual time series 14.4 Least-squares trend fitting and forecasting 14.5 The Holt-Winters method for trend fitting and forecasting 14.6 Autoregressive modelling for trend fitting and forecasting 14.7 Choosing an appropriate forecasting model 14.8 Time-series forecasting of seasonal data 14.9 Index numbers 14.10 Pitfalls in time-series forecasting Summary Key formulas Key terms References Chapter review problems Chapter 14 Excel Guide Chapter 15 Chi-square tests 15.1 Chi-square test for the difference between two proportions (independent samples) 15.2 Chi-square test for differences between more than two proportions 15.3 Chi-square test of independence 15.4 Chi-square goodness-of-fit tests 15.5 Chi-square test for a variance or standard deviation Summary Key formulas Key terms References Chapter review problems Continuing cases Chapter 15 Excel Guide End of Part 4 problems Part 5 Further topics in stats Chapter 16 Multiple regression model building 16.1 Quadratic regression model 16.2 Using transformations in regression models 16.3 Influence analysis 16.4 Model building 16.5 Pitfalls in multiple regression and ethical issues Summary Key formulas Key terms References Chapter review problems Continuing cases Chapter 16 Excel Guide Chapter 17 Decision making 17.1 Payoff tables and decision trees 17.2 Criteria for decision making 17.3 Decision making with sample information 17.4 Utility Summary Key formulas Key terms References Chapter review problems Chapter 17 Excel Guide Chapter 18 Statistical applications in quality management 18.1 Total quality management 18.2 Six Sigma management 18.3 The theory of control charts 18.4 Control chart for the proportion - The p chart 18.5 The red bead experiment - Understanding process variability 18.6 Control chart for an area of opportunity - The c chart 18.7 Control charts for the range and the mean 18.8 Process capability Summary Key formulas Key terms References Chapter review problems Chapter 18 Excel Guide Chapter 19 Further non-parametric tests 19.1 McNemar test for the difference between two proportions (related samples) 19.2 Wilcoxon rank sum test - Non-parametric analysis for two independent populations 19.3 Wilcoxon signed ranks test - Non-parametric analysis for two related populations 19.4 Kruskal-Wallis rank test - Non-parametric analysis for the one-way anova 19.5 Friedman rank test - Non-parametric analysis for the randomised block design Summary Key formulas Key terms Chapter review problems Continuing cases Chapter 19 Excel Guide Chapter 20 Business analytics 20.1 Predictive analytics 20.2 Classification and regression trees 20.3 Neural networks 20.4 Cluster analysis 20.5 Multidimensional scaling Summary Key formulas Key terms References Chapter review problems Chapter 20 Software Guide Chapter 21 Data analysis: The big picture 21.1 Analysing numerical variables 21.2 Analysing categorical variables 21.3 Predictive analytics Chapter review problems End of Part 5 problems Appendices Glossary Index