Business Statistics Abridged: Australia and New Zealand

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 discreteprobability 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 X
		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: Chi-squared tests
		Introduction
		14.1 Chi-squared goodness-of-fit test
		14.2 Chi-squared test of a contingency table
		14.3 Chi-squared test for normality
		14.4 Summary of tests on nominal data
		Chapter summary
		Case 14.1 Gold lotto
		Case 14.2 Exit polls
		Case 14.3 How well is the Australian Government managing the coronavirus pandemic?
		Appendix 14.A:
Chi-squared distribution
	Chapter
15: Simple linear regression and correlation
		Introduction
		15.1 Model
		15.2 Estimating the coefficients
		15.3 Error variable: Required conditions
		15.4 Assessing the model
		15.5 Using the regression equation
		15.6 Testing the coefficient of correlation
		15.7 Regression diagnostics – I
		Chapter summary
		Case 15.1 Does unemployment rate affect weekly earnings
in New Zealand?
		Case 15.2 Tourism vs tax revenue
		Case 15.3 D oes unemployment affect inflation in New Zealand?
		Case 15.4 Does domestic market capital influence stock prices?
		Case 15.5 Book sales vs free examination copies
		Case 15.6 Does increasing per capita income lead to
increase in energy consumption?
		Case 15.7 Market model of share returns
		Case 15.8 Life insurance policies
		CASE 15.9 Education and income: How are they related?
		CASE 15.10 Male and female unemployment rates in New Zealand – Are they related?
	Chapter
16: Multiple regression
		Introduction
		16.1 Model and required conditions
		16.2 Estimating the coefficients and assessing
the model
		16.3 Regression diagnostics – II
		16.4 Regression diagnostics – III (time series)
		Chapter summary
		Case 16.1 Are lotteries a tax on the poor and uneducated?
		Case 16.2 Demand for beer in Australia
		Case 16.3 Book sales vs free examination copies revisited
		Case 16.4 Average hourly earnings in New Zealand
		Case 16.5 Testing a more effective device to keep
arteries open
		Appendix 16.A: F-distribution
Part 3: Applications
	Chapter
17: Time series analysis and forecasting
		Introduction
		17.1 Components of a time series
		17.2 Smoothing techniques
		17.3 Trend analysis
		17.4 Measuring the cyclical effect
		17.5 Measuring the seasonal effect
		17.6 Introduction to forecasting
		17.7 Time series forecasting with exponential
smoothing
		17.8 Time series forecasting with regression
		Chapter summary
		Case 17.1 Part-time employed females
		Case 17.2 New Zealand tourism: Tourist arrivals
		Case 17.3 Seasonal and cyclical effects in number of houses
constructed in Queensland
		Case 17.4 Measuring the cyclical effect on Woolworths‘ stock prices
	Chapter 18: Index numbers
		Introduction
		18.1 Constructing unweighted index numbers
		18.2 Constructing weighted index numbers
		18.3 The Australian Consumer Price Index (CPI)
		18.4 Using the CPI to deflate wages and GDP
		18.5 Changing the base period of an indexnumber series
		Chapter summary
		Case 18.1 Soaring petrol prices in Australian capital cities
		Case 18.2 Is the Australian road toll on the increase again?
Appendix B: Statistical Tables
Appendix A: Summary Solutions for Selected (Even-Numbered) Exercises
Glossary
Index