Data Driven Business Decisions

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ISBN: 9780470619605 | 0470619600
Cover: Hardcover
Copyright: 10/25/2011

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Grounded in a solid business context with an emphasis on data-driven decision making, Data and Decisions for MBAs 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.

How are we doing: Data driven views of business performance
Setting out business data
Different kinds of variables
The idea of a distribution
Typical performance (the mean)
Uncertainty in performance (standard deviation)
Changing units
Shapes of distributions
What stands out and whys? Who Wins? Data driven views of performance dynamics
Two different data layouts
Comparing performance across several segments
Complex comparisons - using pivotables
Unusually high and low outcomes - z scores
Choosing a sensible peer group
Combining different performance measures
Dealing with uncertainty and chance
Framing what could happen: outcomes and events
How likely is it? Probability basics
Market segments and behaviour: Using probability tables
Example in health care: testing for a disease
Changing your assessment with conditional probability
How strong is the relationship? Measuring dependence
Probability trees
Let the data change you views: Bayes Method
Bayes Method in Pictures
Bayes Method as an algorithm
Example 1. A simple gambling game
Example 2. Bayes in the courtroom
Some typical business applications
Valuing an uncertain payoff
What is a probability distribution?
Displaying a probability distribution
The mean of a distribution
Example: Fines and violations
Why use the mean?
The standard deviation of a distribution
Comparing two distributions
Conditional distributions and means
Business problems that depends on knowing "how many"
The binomial distribution
Mean and standard deviation of the binomial
The negative binomial distribution
The Poisson distribution
Some typical business applications
Business problems that depends on knowing "how much"
The normal distribution
Calculating normal probabilities in Excel
Combining normal variables
Comparing normal distributions
the standard normal distribution
Example: Dealing with uncertain demand
Dealing with proportional variation
Making complex decisions with trees
Elements of decision trees
Solving the decision tree
Multistage Decision trees
Valuing a decision option
the cost of uncertainty
Data, estimation and statistical reliability
Describing the past and the future
How was the data generated?
The law of large numbers
The variability of the average
the standard error of the mean
The normal limit theorem
Samples and populations
Managing mean performance
Benchmarking mean performance
The statistical size of a deviation
Decision making, hypothesis testing and P-values
Confidence intervals
One and two sided tests
Using StatproGo
Why standard deviation matters
Assessing detection power
Are these customers different? Did the intervention work? Looking at changes in mean performance
How variable is a difference?
Describing changes in mean performance
Example: Is product placement worth it?
Comparing two means with StatproGo
Different standard deviations
Analysing matched pairs
What is my brand recognition? Will it sell? Analysing counts and proportions
How accuate is a percentage?
Tests and intervals for proportions
Assessing changes in proportions
Comparing proportions with StatproGo
Alternative methods
Using the relationship between shares to build a portfolio
How to measure financial growth
Risk and return - both matter
Correlation and industry structure
The riskness of a portfolio
Balancing risk and return
Controlling risk with TB?s
Investigating relationship between business variables
Measuring association with correlation
Looking at complex relationships
Interpreting correlation
Autocorrelation
Partial correlations
Describing the effect of a business input: Linear regression
Linear relationships
The line of best fit
Computing the least squares line
The regression model
How reliable is the regression line?
The reliability of regression based decisions
Business prediction - three types of questions
Estimating the effect of a change
Estimating the trend mean
Prediction
Prediction errors and what they tell you
Multi-causal relationship and multiple regression
Multi-linear relationships
Multiple regression
Model assessment
Prediction and trend estimation
Product features, non-linear relationships and market segments
Accounting for yes-no features
Quadratic relationships
Quadratic regression
Allowing for segments and groups
Automatic model selection
Analysing data that is collected regularly over time
Measuring growth and seasonality
How is the growth rate changing?
Seasonal adjustment
Delayed effects
Predicting the future (using auto-regression)
Extending regression models - the sky is the limit
Effects that depends on other inputs - interactions
Effects that have proportional impacts
Case study: How effective are catalog mail-outs?
More on time series
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Data Driven Business Decisions

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