Business Statistics: Australia and New Zealand, 8th Edition

by Eliyathamby A Selvanathan, Gerald Keller, Saroja Selvanathan

Length: 1162 pages
Edition: 8
Language: English
Publisher: Cengage Learning
Publication Date: 2021
ISBN-10: 0170439526
ISBN-13: 9780170439527
Sales Rank: #0 (See Top 100 Books)

Business Statistics teaches you skills that you can use throughout your career. It illustrates how vital statistical methods and tools are for today’s managers and analysts, and how to apply them to business problems using real-world data. Statistical data analysis is the backbone of sound business decision making, and finding the right tool to analyse a particular business problem is the key. The book shows you how to analyse data by focusing on the kind of problem you face, the type of data involved and the appropriate technique for solving the problem. It also includes data-driven examples, exercises and cases that cover how marketing managers, financial analysts, accountants, economists and others use statistics. This edition includes the NEW XLStat analysis plugin/tool.

Cover
Half Title page
Dedication page
Title page
Imprint page
Brief contents
Contents
Preface
Guide to the text
Guide to the online resources
Acknowledgements
About the authors
Chapter 1: What is statistics?
	Introduction to statistics
	1.1 Key statistical concepts
	1.2 Statistical applications in business
	Case 3.6 Differing average weekly earnings of men and women in Australia
	Case 4.2 Analysing the spread of the Global Coronavirus Pandemic
	Case 5.5 Sydney and Melbourne lead the way in the growth in house prices
	Case 14.1 Comparing salary offers for finance and marketing MBA majors – I
	Case 16.1 Gold lotto
	Case 17.3 Does unemployment affect inflation in New Zealand?
	1.3 How managers use statistics
	1.4 Statistics and the computer
	1.5 Online resources
	Appendix 1.A Introduction to Microsoft Excel
Chapter 2: Types of data, data collection and sampling
	Introduction
	2.1 Types of data
	2.2 Methods of collecting data
	2.3 Sampling
	2.4 Sampling plans
	2.5 Sampling and non-sampling errors
	Chapter summary
Part 1: Descriptive measures and probability
	Chapter 3: Graphical descriptive techniques – Nominal data
		Introduction
		3.1 Graphical techniques to describe nominal data
		3.2 Describing the relationship between two nominal variables
		Chapter summary
		Case 3.1 Analysing the COVID-19 deaths in Australia by gender and age group
		Case 3.2 Corporate tax rates around the world
		Case 3.3 Trends in CO2 emissions
		Case 3.4 Where is the divorce rate heading?
		Case 3.5 Geographic location of share ownership in Australia
		Case 3.6 Differing average weekly earnings of men and women in Australia
		Case 3.7 The demography of Australia
		Case 3.8 Survey of graduates
		Case 3.9 Analysing the health effect of the Coronavirus pandemic
		Case 3.10 Australian domestic and overseas student market by states and territories
		Case 3.11 Road fatalities in Australia
		Case 3.12 Drinking behaviour of Australians
	Chapter 4: Graphical descriptive techniques – Numerical data
		Introduction
		4.1 Graphical techniques to describe numerical data
		4.2 Describing time-series data
		4.3 Describing the relationship between two or more numerical variables
		4.4 Graphical excellence and deception
		Chapter summary
		Case 4.1 The question of global warming
		Case 4.2 Analysing the spread of the global coronavirus pandemic
		Case 4.3 An analysis of telephone bills
		Case 4.4 An analysis of monthly retail turnover in Australia
		Case 4.5 Economic freedom and prosperity
	Chapter 5: Numerical descriptive measures
		Introduction
		5.1 Measures of central location
		5.2 Measures of variability
		5.3 Measures of relative standing and box plots
		5.4 Measures of association
		5.5 General guidelines on the exploration of data
		Chapter summary
		Case 5.1 Return to the global warming question
		Case 5.2 Another return to the global warming question
		Case 5.3 GDP versus consumption
		Case 5.4 The gulf between the rich and the poor
		Case 5.5 Sydney and Melbourne leading the way in the growth in house prices
		Case 5.6 Performance of managed funds in Australia: 3-star, 4-star and 5-star rated funds
		Case 5.7 Life in suburbs drives emissions higher
		Case 5.8 Aussies and Kiwis are leading in education
		Case 5.9 Growth in consumer prices and consumption in Australian states
		Appendix 5.A Summation notation
		Appendix 5.B Descriptive measures for grouped data
	Chapter 6: Probability
		Introduction
		6.1 Assigning probabilities to events
		6.2 Joint, marginal and conditional probability
		6.3 Rules of probability
		6.4 Probability trees
		6.5 Bayes’ law
		6.6 Identifying the correct method
		Chapter summary
		Case 6.1 Let’s make a deal
		Case 6.2 University admissions in Australia: Does gender matter?
		Case 6.3 Maternal serum screening test for Down syndrome
		Case 6.4 Levels of disability among children in Australia
		Case 6.5 Probability that at least two people in the same room have the same birthday
		Case 6.6 Home ownership in Australia
		Case 6.7 COVID-19 confirmed cases and deaths in Australia II
	Chapter 7: Random variables and discrete probability distributions
		Introduction
		7.1 Random variables and probability distributions
		7.2 Expected value and variance
		7.3 Binomial distribution
		7.4 Poisson distribution
		7.5 Bivariate distributions
		7.6 Applications in finance: Portfolio diversification and asset allocation
		Chapter summary
		Case 7.1 Has there been a shift in the location of overseas-born population within Australia over the 50 years from 1996 to 2016?
		Case 7.2 How about a carbon tax on motor vehicle ownership?
		Case 7.3 How about a carbon tax on motor vehicle ownership? – New Zealand
		Case 7.4 Internet usage by children
		Case 7.5 COVID-19 deaths in Australia by age and gender III
	Chapter 8: Continuous probability distributions
		Introduction
		8.1 Probability density functions
		8.2 Uniform distribution
		8.3 Normal distribution
		8.4 Exponential distribution
		Chapter summary
		Case 8.1 Average salary of popular business professions in Australia
		Case 8.2 Fuel consumption of popular brands of motor vehicles
		Appendix 8.A Normal approximation to the binomial distribution
Part 2: Statistical inference
	Chapter 9: Statistical inference and sampling distributions
		Introduction
		9.1 Data type and problem objective
		9.2 Systematic approach to statistical inference: A summary
		9.3 Introduction to sampling distribution
		9.4 Sampling distribution of the sample mean ˉX
		9.5 Sampling distribution of the sample proportion p
		9.6 From here to inference
		Chapter summary
	Chapter 10: Estimation: Single population
		Introduction
		10.1 Concepts of estimation
		10.2 Estimating the population mean μ when the population variance σ2 is known
		10.3 Estimating the population mean μ when the population variance σ2 is unknown
		10.4 Estimating the population proportion p
		10.5 Determining the required sample size
		10.6 Applications in marketing: Market segmentation
		Chapter summary
		Case 10.1 Estimating the monthly average petrol price in Queensland
		Case 10.2 Cold men and cold women will live longer!
		Case 10.3 Super fund managers letting down retirees
		Appendix 10.A Excel instructions for missing data and for recoding data
	Chapter 11: Estimation: Two populations
		Introduction
		11.1 Estimating the difference between two population means (μ1 − μ2) when the population variances are known: Independent samples
		11.2 Estimating the difference between two population means (μ1 − μ2) when the population variances are unknown: Independent samples
		11.3 Estimating the difference between two population means with matched pairs experiments: Dependent samples
		11.4 Estimating the difference between two population proportions, p1 – p2
		Chapter summary
		Case 11.1 Has demand for print newspapers declined in Australia?
		Case 11.2 Hotel room prices in Australia: Are they becoming cheaper?
		Case 11.3 Comparing hotel room prices in New Zealand
		Case 11.4 Comparing salary offers for finance and marketing major graduates
		Case 11.5 Estimating the cost of a life saved
	Chapter 12: Hypothesis testing: Single population
		Introduction
		12.1 Concepts of hypothesis testing
		12.2 Testing the population mean μ when the population variance σ2 is known
		12.3 The p-value of a test of hypothesis
		12.4 Testing the population mean μ when the population variance σ2 is unknown
		12.5 Calculating the probability of a Type II error
		12.6 Testing the population proportion p
		Chapter summary
		Case 12.1 Singapore Airlines has done it again
		Case 12.2 Australian rate of real unemployment
		Case 12.3 The republic debate: What Australians are thinking
		Case 12.4 Has Australian Business Confidence improved since the May 2019 election?
		Case 12.5 Is there a gender bias in the effect of COVID-19 infection?
		Appendix 12.A Excel instructions
	Chapter 13: Hypothesis testing: Two populations
		Introduction
		13.1 Testing the difference between two population means: Independent samples
		13.2 Testing the difference between two population means: Dependent samples – matched pairs experiment
		13.3 Testing the difference between two population proportions
		Chapter summary
		Case 13.1 Is there gender difference in spirits consumption?
		Case 13.2 Consumer confidence in New Zealand
		Case 13.3 New Zealand Government bond yields: Short term versus long term
		Case 13.4 The price of petrol in Australia: Is it similar across regions?
		Case 13.5 Student surrogates in market research
		Case 13.6 Do expensive drugs save more lives?
		Case 13.7 Comparing two designs of ergonomic desk: Part I
		Appendix 13.A Excel instructions: Manipulating data
	Chapter 14: Inference about population variances
		Introduction
		14.1 Inference about σ2
		14.2 Inference about σ2 1/σ2 2
		Chapter summary
		Case 14.1 Comparing salary offers for finance and marketing MBA majors – I
		Case 14.2 Comparing salary offers for finance and marketing MBA majors – II
		Case 14.3 Risk of an asset: Five-year bonds versus 10-year bonds
		Case 14.4 Efficiency in streetlight maintenance
		Case 14.5 Comparing two designs of ergonomic desk II
		Appendix 14.A A brief discussion of the derivation of the chi-squared distribution
	Chapter 15: Analysis of variance
		Introduction
		15.1 Single-factor analysis of variance: Independent samples (one-way ANOVA)
		15.2 Multiple comparisons
		15.3 Analysis of variance: Experimental designs
		15.4 Single-factor analysis of variance: Randomised blocks (two-way ANOVA)
		15.5 Two-factor analysis of variance
		Chapter summary
		Case 15.1 Top 300 Australian companies
		Case 15.2 Which diets work?
		Case 15.3 Effects of financial planning on small businesses
		Case 15.4 Diversification strategy for multinational companies
		Case 15.5 Comparing three methods of treating childhood ear infections
		Case 15.6 Treatment of headaches
	Chapter 16: Chi-squared tests
		Introduction
		16.1 Chi-squared goodness-of-fit test
		16.2 Chi-squared test of a contingency table
		16.3 Chi-squared test for normality
		16.4 Summary of tests on nominal data
		Chapter summary
		Case 16.1 Gold lotto
		Case 16.2 Exit polls
		Case 16.3 How well is the Australian Government managing the coronavirus pandemic?
		Appendix 16.A Chi-squared test for a Poisson distribution
		Appendix 16.B Review of Chapters 10 to 16
	Chapter 17: Simple linear regression and correlation
		Introduction
		17.1 Model
		17.2 Estimating the coefficients
		17.3 Error variable: Required conditions
		17.4 Assessing the model
		17.5 Using the regression equation
		17.6 Testing the coefficient of correlation
		17.7 Regression diagnostics – I
		Chapter summary
		Case 17.1 Does unemployment rate affect weekly earnings in New Zealand?
		Case 17.2 Tourism vs tax revenue
		Case 17.3 Does unemployment affect inflation in New Zealand?
		Case 17.4 Does domestic market capital influence stock prices?
		Case 17.5 Book sales vs free examination copies
		Case 17.6 Does increasing per capita income lead to increase in energy consumption?
		Case 17.7 Market model of share returns
		Case 17.8 Life insurance policies
		Case 17.9 Education and income: How are they related?
		Case 17.10 Male and female unemployment rates in New Zealand – Are they related?
	Chapter 18: Multiple regression
		Introduction
		18.1 Model and required conditions
		18.2 Estimating the coefficients and assessing the model
		18.3 Regression diagnostics – II
		18.4 Regression diagnostics – III (time series)
		Chapter summary
		Case 18.1 Are lotteries a tax on the poor and uneducated?
		Case 18.2 Demand for beer in Australia
		Case 18.3 Book sales vs free examination copies revisited
		Case 18.4 Average hourly earnings in New Zealand
		Case 18.5 Testing a more effective device to keep arteries open
	Chapter 19: Model building
		Introduction
		19.1 Polynomial models
		19.2 Nominal independent variables
		19.3 Applications in human resources: Pay equity (Optional)
		19.4 Variable selection methods (Optional)
		19.5 Model building
		Chapter summary
		Case 19.1 Does the street number of a house matter?
		Case 19.2 MBA program admissions policy
		Case 19.3 Track and field performance forecasts
		Appendix 19.A Logistic regression
	Chapter 20: Nonparametric techniques
		Introduction
		20.1 Wilcoxon rank sum test
		20.2 Sign test and Wilcoxon signed rank sum test
		20.3 Kruskal–Wallis test and Friedman test
		20.4 Spearman rank correlation coefficient
		Chapter summary
		Case 20.1 HR Information Systems Inc.
		Case 20.2 Bank of Commerce customer survey
	Chapter 21: Statistical inference: Conclusion
		Introduction
		21.1 Identifying the correct technique: Summary of statistical inference
		21.2 Beyond the statistics subject – the last word
		Case 21.1 Do banks discriminate against women business owners? – Part I
		Case 21.2 Do banks discriminate against women business owners? – Part II
		Case 21.3 Graduate survey report
		Case 21.4 Effect of the death of key executives on stock market returns
		Case 21.5 Evaluation of a new drug
		Case 21.6 Nutrition education programs
		Case 21.7 Type A, B and C personalities and job satisfaction and performance
Part 3: Applications
	Chapter 22: Time series analysis and forecasting
		Introduction
		22.1 Components of a time series
		22.2 Smoothing techniques
		22.3 Trend analysis
		22.4 Measuring the cyclical effect
		22.5 Measuring the seasonal effect
		22.6 Introduction to forecasting
		22.7 Time series forecasting with exponential smoothing
		22.8 Time series forecasting with regression
		Chapter summary
		Case 22.1 Part-time employed females
		Case 22.2 New Zealand tourism: Tourist arrivals
		Case 22.3 Seasonal and cyclical effects in number of houses constructed in Queensland
		Case 22.4 Measuring the cyclical effect on Woolworths’ stock prices
	Chapter 23: Index numbers
		Introduction
		23.1 Constructing unweighted index numbers
		23.2 Constructing weighted index numbers
		23.3 The Australian Consumer Price Index (CPI)
		23.4 Using the CPI to deflate wages and GDP
		23.5 Changing the base period of an index number series
		Chapter summary
		Case 23.1 Soaring petrol prices in Australian capital cities
		Case 23.2 Is the Australian road toll on the increase again?
Appendix A: Summary Solutions for Selected (Even-Numbered) Exercises
Appendix B: Statistical Tables
Glossary
Index