Grounded in a solid business context with an emphasis on data-driven decision making, Data and Decisions for MBA''s presents a down-to-earth treatment of the essentials of statistics. The book introduces chapters with a deeply contextual motivating example, followed by further details, raw data, and motivating insights. The author includes algebraic notation only when necessary and/or useful and presents both the pros and cons of statistical methods. Excel, StatPro, and Treeplan are showcased throughout the book for MBA students at the beginning graduate level or for on-the-job practitioners.

CHRIS J. LLOYD, PhD, is Associate Dean of Research and Professor of Business Statistics in the Melbourne Business School at The University of Melbourne, Australia. Professor Lloyd has extensive international academic and consulting experience in the fields of statistics, data analysis, and market research within both academic and business environments. He has written more than 100 research articles in the areas of categorical data and is the author of Statistical Analysis of Categorical Data, also published by Wiley.

Chapter 1. How are we doing: Data driven views of business performance. 1.1 Setting out business data. 1.2 Different kinds of variables. 1.3 The idea of a distribution. 1.4 Typical performance (the mean). 1.5 Uncertainty in performance (standard deviation). 1.6 Changing units. 1.7 Shapes of distributions. Chapter 2. What stands out and why? Who Wins? Data driven views of performance dynamics. 2.1 Different layouts of business data. 2.2 Comparing performance across several segments. 2.3 Complex comparisons - using pivotables. 2.4 Unusually high and low outcomes - z scores. 2.5 Choosing a sensible peer group. 2.6 Combining different performance measures. Chapter 3. Dealing with uncertainty and chance. 3.1 Framing what could happen: outcomes and events. 3.2 How likely is it? Probability basics. 3.3 Market segments and behaviour: Using probability tables. 3.4 Example in health care: testing for a disease. 3.5 Changing your assessment with conditional probability. 3.6 How strong is the relationship? Measuring dependence. 3.7 Probability trees. Chapter 4. Let the data change you views: Bayes Method. 4.1 Bayes Method in Pictures. 4.2 Bayes Method as an algorithm. 4.3 Example 1. A simple gambling game. 4.4 Example 2. Bayes in the courtroom. 4.5 Some typical business applications. Chapter 5. Valuing an uncertain payoff. 5.1 What is a probability distribution? 5.2 Displaying a probability distribution. 5.3 The mean of a distribution. 5.4 Example: Fines and violations. 5.5 Why use the mean? 5.6 The standard deviation of a distribution. 5.7 Comparing two distributions. 5.8 Conditional distributions and means. Chapter 6. Business problems that depends on knowing "how many". 6.1 The binomial distribution. 6.2 Mean and standard deviation of the binomial. 6.3 The negative binomial distribution. 6.4 The Poisson distribution. 6.5 Some typical business applications. Chapter 7. Business problems that depends on knowing "how much". 7.1 The normal distribution. 7.2 Calculating normal probabilities in Excel. 7.3 Combining normal variables. 7.4 Comparing normal distributions. 7.5 The standard normal distribution. 7.6 Example: Dealing with uncertain demand. 7.7 Dealing with proportional variation. Chapter 8. Making complex decisions with trees. 8.1 Elements of decision trees. 8.2 Solving the decision tree. 8.3 Multistage Decision trees. 8.4 Valuing a decision option. 8.5 The cost of uncertainty. Chapter 9. Data, estimation and statistical reliability. 9.1 Describing the past and the future. 9.2 How was the data generated? 9.3 The law of large numbers. 9.4 The variability of the average. 9.5 The standard error of the mean. 9.6 The normal limit theorem. 9.7 Samples and populations. Chapter 10. Managing mean performance. 10.1 Benchmarking mean performance. 10.2 The statistical size of a deviation. 10.3 Decision making, hypothesis testing and P-values. 10.4 Confidence intervals. 10.5 One and two sided tests. 10.6 Using StatproGo. 10.7 Why standard deviation matters. 10.8 Assessing detection power. Chapter 11. Are these customers different? Did the intervention work? Looking at changes in mean performance. 11.1 How variable is a difference? 11.2 Describing changes in mean performance. 11.3 Example: Is product placement worth it? 11.4 Comparing two means with StatproGo. 11.5 Different standard deviations. 11.6 Analysing matched pairs data. Chapter 12. What is my brand recognition? ...

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