Basic business statistics: concepts and applications

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