Better Business Decisions from Data

Everyone encounters statistics on a daily basis. They are used in proposals, reports, requests, and advertisements, among others, to support assertions, opinions, and theories. Unless you’re a trained statistician, it can be bewildering. What are the numbers really saying or not saying? Better Business Decisions from Data: Statistical Analysis for Professional Success provides the answers to these questions and more. It will show you how to use statistical data to improve small, every-day management judgments as well as major business decisions with potentially serious consequences.

Author Peter Kenny—with deep experience in industry—believes that “while the methods of statistics can be complicated, the meaning of statistics is not.” He first outlines the ways in which we are frequently misled by statistical results, either because of our lack of understanding or because we are being misled intentionally. Then he offers sound approaches for understanding and assessing statistical data to make excellent decisions. Kenny assumes no prior knowledge of statistical techniques; he explains concepts simply and shows how the tools are used in various business situations.

With the arrival of Big Data, statistical processing has taken on a new level of importance. Kenny lays a foundation for understanding the importance and value of Big Data, and then he shows how mined data can help you see your business in a new light and uncover opportunity.

Among other things, this book covers:

• How statistics can help you assess the probability of a successful outcome
• How data is collected, sampled, and best interpreted
• How to make effective forecasts based on the data at hand
• How to spot the misuse or abuse of statistical evidence in advertisements, reports, and proposals
• How to commission a statistical analysis

Arranged in seven parts—Uncertainties, Data, Samples, Comparisons, Relationships, Forecasts, and Big Data—Better Business Decisions from Data is a guide for busy people in general management, finance, marketing, operations, and other business disciplines who run across statistics on a daily or weekly basis. You’ll return to it again and again as new challenges emerge, making better decisions each time that boost your organization’s fortunes—as well as your own.

What you’ll learn

• How raw data are processed to obtain information, with known reliability, for the basis of decision making.
• What a statistical analysis can–and can’t–do.
• Why certainty is illusive and how we can be misled by statistical results.
• The basics of probability, sampling, reliability, regression, distribution and other statistical techniques essential for decision making in all aspects of business.
• How to commission data gathering and processing in advance of big decisions

Who this book is for

The primary audience includes managers and professionals in business and industry who need to understand statistics to make or approve decisions, or to commission statistical investigations and assess their results. It’s also for those who want to understand how statistics can be used to mislead or shroud the true facts. A secondary audience consists of students of disciplines that require some knowledge of statistics—economics, finance, political science, physics, biology, and more—as well as general readers who simply wish to have a more informed view of the daily dose of statistics offered up by news organizations, advocacy groups, and the government, among others.

Table of Contents

Part I: Uncertainties
Chapter 1: The Scarcity of Certainty
Chapter 2: Sources of Uncertainty
Chapter 3: Probability

Part II: Data
Chapter 4: Sampling
Chapter 5: The Raw Data

Part III: SamplesThe
Chapter 6: Descriptive Data
Chapter 7: Numerical Data

Part IV: Comparisons
Chapter 8: Levels of Significance
Chapter 9: General Procedure for Comparisons
Chapter 10: Comparisons with Numerical Data
Chapter 11: Comparisons with Descriptive Data
Chapter 12: Types of Error

Part V: Relationships
Chapter 13: Cause and Effect
Chapter 14: Relationships with Numerical Data
Chapter 15: Relationships with Descriptive Data
Chapter 16: Multivariate Data

Part VI: Forecasts
Chapter 17: Extrapolation
Chapter 18: Forecasting from Known Distributions
Chapter 19: Time Series
Chapter 20: Control Charts
Chapter 21: Reliability

Part VII: Big Data
Chapter 22: Data Mining
Chapter 23: Predictive Analytics
Chapter 24: Getting Involved with Big Data
Chapter 25: Concerns with Big Data
Chapter 26: References and Further Reading

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