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

✍ Scribed by Mark L. Berenson; David M. Levine; Kathryn A. Szabat; Martin O’Brien; Nicola Jayne; Judith Watson

• Publisher: Pearson Australia
• Year: 2019
• Tongue: English
• Leaves: 889
• Edition: 5
• Category: Library

✦ Synopsis

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

✦ Table of Contents

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