  Lloyd, Chris J. Data Driven Business Decisions Statistics in Practice
1. Edition November 2011 115. Euro 2011. 512 Pages, Hardcover ISBN 9780470619605  John Wiley & Sons

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 Short description Grounded in a solid business context with an emphasis on datadriven decision making, Data and Decisions for MBA's presents a downtoearth 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 onthejob practitioners.
From the contents 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 Pvalues.
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? Will it sell? Analysing counts and proportions.
12.1 How accurate is a percentage?
12.2 Tests and intervals for proportions.
12.3 Assessing changes in proportions.
12.4 Comparing proportions with StatproGo.
12.5 Alternative methods.
Chapter 13. Using the relationship between shares to build a portfolio.
13.1 How to measure financial growth.
13.2 Risk and return  both matter.
13.3 Correlation and industry structure.
13.4 The riskiness of a portfolio.
13.5 Balancing risk and return.
13.6 Controlling risk with TB's.
Chapter 14. Investigating relationship between business variables.
14.1 Measuring association with correlation.
14.2 Looking at complex relationships.
14.3 Interpreting correlation.
14.4 Autocorrelation.
14.5 Untangling relationships with partial correlations.
Chapter 15. Describing the effect of a business input: Linear regression.
15.1 Linear relationships.
15.2 The line of best fit.
15.3 Computing the least squares line.
15.4 The regression model.
15.5 How reliable is the regression line?
Chapter 16. The reliability of regression based decisions.
16.1 Business prediction  three types of questions.
16.2 Estimating the effect of a change.
16.3 Estimating the trend mean.
16.4 Prediction.
16.5 Prediction errors and what they tell you.
Chapter 17. Multicausal relationship and multiple regression.
17.1 Multilinear relationships.
17.2 Multiple regression.
17.3 Model assessment.
17.4 Prediction and trend estimation.
Chapter 18. Product features, nonlinear relationships and market segments.
18.1 Accounting for yesno features.
18.2 Quadratic relationships.
18.3 Quadratic regression.
18.4 Allowing for segments and groups.
18.5 Automatic model selection.
Chapter 19. Analysing data that is collected regularly over time.
19.1 Measuring growth and seasonality.
19.2 How is the growth rate changing?
19.3 Seasonal adjustment.
19.4 Delayed effects.
19.5 Predicting the future (using autoregression).
Chapter 20. Extending regression models  the sky is the limit.
20.1 Effects that depends on other inputs  interactions.
20.2 Effects that have proportional impacts.
20.3 Case study: How effective are catalog mailouts?
20.4 More on time series.


 