Business Statistics Abridged: Australia and New Zealand, 8th Edition
By Eliyathamby A Selvanathan, Saroja Selvanathan and Gerald Keller
Contents:
Preface XII
Guide to the text XVI
Guide to the online Resources XX
Acknowledgement XXII
About the Authors XXIII
1 What is statistics? 1
Introduction to statistics 2
1.1 Key statistical concepts 5
1.2 Statistical applications in business 6
Case 3.6 Differing average weekly earnings of men and women in Australia 7
Case 4.2 Analysing the spread of the Global Coronavirus Pandemic 7
Case 5.5 Sydney and Melbourne lead the way in the growth in house prices 7
Case 14.1 Comparing salary offers for finance and marketing MBA majors – I 8
Case 16.1 Gold lotto 8
Case 17.3 Does unemployment affect inflation in New Zealand? 9
1.3 How managers use statistics 9
1.4 Statistics and the computer 11
1.5 Online resources 13
Appendix 1.A Introduction to Microsoft Excel 15
2 Types of data, data collection and sampling 18
Introduction 19
2.1 Types of data 20
2.2 Methods of collecting data 26
2.3 Sampling 30
2.4 Sampling plans 32
2.5 Sampling and non-sampling errors 39
Chapter summary 41
PART 1: DESCRIPTIVE MEASURES AND PROBABILITY 43
3 Graphical descriptive techniques – Nominal data 44
Introduction 45
3.1 Graphical techniques to describe nominal data 46
3.2 Describing the relationship between two nominal variables 68
Chapter summary 74
Case 3.1 Analysing the COVID-19 deaths in Australia by gender and age group 77
Case 3.2 Corporate tax rates around the world 77
Case 3.3 Trends in CO2 emissions 78
Case 3.4 Where is the divorce rate heading? 79
Case 3.5 Geographic location of share ownership in Australia 80
Case 3.6 Differing average weekly earnings of men and women in Australia 80
Case 3.7 The demography of Australia 81
Case 3.8 Survey of graduates 82
Case 3.9 Analysing the health effect of the Coronavirus pandemic 82
Case 3.10 Australian domestic and overseas student market by states and territories 82
Case 3.11 Road fatalities in Australia 83
Case 3.12 Drinking behaviour of Australians 84
4 Graphical descriptive techniques – Numerical data 85
Introduction 86
4.1 Graphical techniques to describe numerical data 86
4.2 Describing time-series data 106
4.3 Describing the relationship between two or more numerical variables 111
4.4 Graphical excellence and deception 123
Chapter summary 131
Case 4.1 The question of global warming 133
Case 4.2 Analysing the spread of the global coronavirus pandemic 134
Case 4.3 An analysis of telephone bills 134
Case 4.4 An analysis of monthly retail turnover in Australia 134
Case 4.5 Economic freedom and prosperity 134
5 Numerical descriptive measures 135
Introduction 136
5.1 Measures of central location 136
5.2 Measures of variability 153
5.3 Measures of relative standing and box plots 169
5.4 Measures of association 179
5.5 General guidelines on the exploration of data 193
Chapter summary 195
Case 5.1 Return to the global warming question 199
Case 5.2 Another return to the global warming question 199
Case 5.3 GDP versus consumption 199
Case 5.4 The gulf between the rich and the poor 199
Case 5.5 Sydney and Melbourne leading the way in the growth in house prices 200
Case 5.6 Performance of managed funds in Australia: 3-star, 4-star and
5-star rated funds 200
Case 5.7 Life in suburbs drives emissions higher 201
Case 5.8 Aussies and Kiwis are leading in education 202
Case 5.9 Growth in consumer prices and consumption in Australian states 202
Appendix 5.A Summation notation 203
Appendix 5.B Descriptive measures for grouped data 206
6 Probability 211
Introduction 212
6.1 Assigning probabilities to events 212
6.2 Joint, marginal and conditional probability 224
6.3 Rules of probability 234
6.4 Probability trees 239
6.5 Bayes’ law 244
6.6 Identifying the correct method 251
Chapter summary 252
Case 6.1 Let’s make a deal 255
Case 6.2 University admissions in Australia: Does gender matter? 255
Case 6.3 Maternal serum screening test for Down syndrome 255
Case 6.4 Levels of disability among children in Australia 256
Case 6.5 Probability that at least two people in the same room have the same birthday 257
Case 6.6 Home ownership in Australia 257
Case 6.7 COVID-19 confirmed cases and deaths in Australia II 259
7 Random variables and discrete probability distributions 260
Introduction 261
7.1 Random variables and probability distributions 261
7.2 Expected value and variance 269
7.3 Binomial distribution 275
7.4 Poisson distribution 284
7.5 Bivariate distributions 290
7.6 Applications in finance: Portfolio diversification and asset allocation 296
Chapter summary 303
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? 306
Case 7.2 How about a carbon tax on motor vehicle ownership? 306
Case 7.3 How about a carbon tax on motor vehicle ownership? – New Zealand 307
Case 7.4 Internet usage by children 307
Case 7.5 COVID-19 deaths in Australia by age and gender III 308
8 Continuous probability distributions 309
Introduction 310
8.1 Probability density functions 310
8.2 Uniform distribution 313
8.3 Normal distribution 316
8.4 Exponential distribution 336
Chapter summary 341
Case 8.1 Average salary of popular business professions in Australia 343
Case 8.2 Fuel consumption of popular brands of motor vehicles 343
Appendix 8.A Normal approximation to the binomial distribution 344
PART 2: STATISTICAL INFERENCE 349
9 Statistical inference and sampling distributions 350
Introduction 351
9.1 Data type and problem objective 351
9.2 Systematic approach to statistical inference: A summary 352
9.3 Introduction to sampling distribution 354
9.4 Sampling distribution of the sample mean X — 354
9.5 Sampling distribution of the sample proportion ˆp 366
9.6 From here to inference 369
Chapter summary 371
10 Estimation: Single population 373
Introduction 374
10.1 Concepts of estimation 375
10.2 Estimating the population mean μ when the population variance σ 2 is known 378
10.3 Estimating the population mean μ when the population variance σ2 is unknown 391
10.4 Estimating the population proportion p 403
10.5 Determining the required sample size 410
10.6 Applications in marketing: Market segmentation 417
Chapter summary 422
Case 10.1 Estimating the monthly average petrol price in Queensland 426
Case 10.2 Cold men and cold women will live longer! 426
Case 10.3 Super fund managers letting down retirees 427
Appendix 10.A Excel instructions for missing data and for recoding data 428
11 Estimation: Two populations 429
Introduction 430
11.1 Estimating the difference between two population means (μ1 − μ2) when the
population variances are known: Independent samples 431
11.2 Estimating the difference between two population means (μ1 − μ2) when
the population variances are unknown: Independent samples 439
11.3 Estimating the difference between two population means with matched pairs
experiments: Dependent samples 449
11.4 Estimating the difference between two population proportions, p1 – p2 453
Chapter summary 461
Case 11.1 Has demand for print newspapers declined in Australia? 463
Case 11.2 Hotel room prices in Australia: Are they becoming cheaper? 463
Case 11.3 Comparing hotel room prices in New Zealand 464
Case 11.4 Comparing salary offers for finance and marketing major graduates 464
Case 11.5 Estimating the cost of a life saved 465
12 Hypothesis testing: Single population 466
Introduction 467
12.1 Concepts of hypothesis testing 467
12.2 Testing the population mean μ when the population variance σ 2 is known 476
12.3 The p-value of a test of hypothesis 491
12.4 Testing the population mean μ when the population variance σ 2 is unknown 504
12.5 Calculating the probability of a Type II error 510
12.6 Testing the population proportion p 517
Chapter summary 524
Case 12.1 Singapore Airlines has done it again 527
Case 12.2 Australian rate of real unemployment 527
Case 12.3 The republic debate: What Australians are thinking 527
Case 12.4 Has Australian Business Confidence improved since the May 2019 election? 528
Case 12.5 Is there a gender bias in the effect of COVID-19 infection? 528
Appendix 12.A Excel instructions 529
13 Hypothesis testing: Two populations 530
Introduction 531
13.1 Testing the difference between two population means: Independent samples 531
13.2 Testing the difference between two population means: Dependent
samples – matched pairs experiment 551
13.3 Testing the difference between two population proportions 562
Chapter summary 573
Case 13.1 Is there gender difference in spirits consumption? 578
Case 13.2 Consumer confidence in New Zealand 578
Case 13.3 New Zealand Government bond yields: Short term versus long term 579
Case 13.4 The price of petrol in Australia: Is it similar across regions? 579
Case 13.5 Student surrogates in market research 579
Case 13.6 Do expensive drugs save more lives? 580
Case 13.7 Comparing two designs of ergonomic desk: Part I 580
Appendix 13.A Excel instructions: Manipulating data 581
14 Chi-squared tests 582
Introduction 583
14.1 Chi-squared goodness-of-fit test 583
14.2 Chi-squared test of a contingency table 593
14.3 Chi-squared test for normality 608
14.4 Summary of tests on nominal data 614
Chapter summary 616
Case 14.1 Gold lotto 620
Case 14.2 Exit polls 620
Case 14.3 How well is the Australian Government managing the coronavirus pandemic? 620
Appendix 14.A Chi-squared distribution 622
15 Simple linear regression and correlation 624
Introduction 625
15.1 Model 626
15.2 Estimating the coefficients 628
15.3 Error variable: Required conditions 641
15.4 Assessing the model 643
15.5 Using the regression equation 659
15.6 Testing the coefficient of correlation 664
15.7 Regression diagnostics – I 667
Chapter summary 677
Case 15.1 Does unemployment rate affect weekly earnings in New Zealand? 683
Case 15.2 Tourism vs tax revenue 683
Case 15.3 Does unemployment affect inflation in
New Zealand? 683
Case 15.4 Does domestic market capital influence stock prices? 683
Case 15.5 Book sales vs free examination copies 683
Case 15.6 Does increasing per capita income lead to increase in energy consumption? 684
Case 15.7 Market model of share returns 684
Case 15.8 Life insurance policies 685
Case 15.9 Education and income: How are they related? 685
Case 15.10 Male and female unemployment rates in
New Zealand – Are they related? 685
16 Multiple regression 686
Introduction 687
16.1 Model and required conditions 687
16.2 Estimating the coefficients and assessing the model 688
16.3 Regression diagnostics – II 714
16.4 Regression diagnostics – III (time series) 726
Chapter summary 736
Case 16.1 Are lotteries a tax on the poor and uneducated? 741
Case 16.2 Demand for beer in Australia 741
Case 16.3 Book sales vs free examination copies revisited 741
Case 16.4 Average hourly earnings in New Zealand 742
Case 16.5 Testing a more effective device to keep arteries open 742
Appendix 16.A F-distribution 743
PART 3: APPLICATIONS 747
17 Time series analysis and forecasting 748
Introduction 749
17.1 Components of a time series 749
17.2 Smoothing techniques 753
17.3 Trend analysis 763
17.4 Measuring the cyclical effect 768
17.5 Measuring the seasonal effect 772
17.6 Introduction to forecasting 780
17.7 Time series forecasting with exponential smoothing 783
17.8 Time series forecasting with regression 785
Chapter summary 796
Case 17.1 Part-time employed females 798
Case 17.2 New Zealand tourism: Tourist arrivals 798
Case 17.3 Seasonal and cyclical effects in number of houses constructed in Queensland 798
Case 17.4 Measuring the cyclical effect on Woolworths‘ stock prices 798
18 Index numbers 799
Introduction 800
18.1 Constructing unweighted index numbers 801
18.2 Constructing weighted index numbers 808
18.3 The Australian Consumer Price Index (CPI) 812
18.4 Using the CPI to deflate wages and GDP 816
18.5 Changing the base period of an index number series 821
Chapter summary 824
Case 18.1 Soaring petrol prices in Australian capital cities 828
Case 18.2 Is the Australian road toll on the increase again? 828
A : Summary Solutions For Selected
(Even-Numbered) Exercises 829
Appendix B : Statistical Tables 8 4 3
Glossary 864
Index 869