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Über dieses Buch

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 outcomeHow data is collected, sampled, and best interpretedHow to make effective forecasts based on the data at handHow to spot the misuse or abuse of statistical evidence in advertisements, reports, and proposalsHow 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.

Inhaltsverzeichnis

Frontmatter

Uncertainties

Frontmatter

Chapter 1. The Scarcity of Certainty

What Time Will the Next Earthquake Be?
Abstract
On the twenty-second of October, 2012, in Italy, six geophysicists and a government civil protection officer were sentenced to six years in prison on charges of manslaughter for underestimating the risk of a serious earthquake in the vicinity of the city of L’Aquila. Following several seismic shocks, the seven had met in committee on March 31, 2009, to consider the risk of a major earthquake. They recorded three main conclusions: that earthquakes are not predictable, that the L’Aquila region has the highest seismic risk in Italy, and that a large earthquake in the short term was unlikely. On April 6, a major earthquake struck with the loss of more than 300 lives.
Peter Kenny

Chapter 2. Sources of Uncertainty

Why “Sure Thing!” Rarely Is
Abstract
The results of any investigation will, of course, be uncertain, if not completely wrong, if the information on which the investigation is based is not correct. However, in statistical investigations there are additional sources of uncertainty, because of the need to extract a neat and useful conclusion from information that may be extensive and variable.
Peter Kenny

Chapter 3. Probability

How Bad Statistics Can Put You in Jail
Abstract
To appreciate statistical analysis it is necessary to have some understanding of probability. Surprisingly, perhaps, not very much is required. Knowing how several different probabilities work together in combination and how the probability of occurrence of an event is affected by an overriding condition are all that are needed for most purposes.
Peter Kenny

Data

Frontmatter

Chapter 4. Sampling

Did Nine out of Ten Really Say That?
Abstract
An essential feature of a sample is that it is representative of the population from which it is drawn. Unfortunately, it is impossible to predict that this will be so, or even check that it is so when the sample has been obtained. A judgment has to be made as to the adequacy of the sampling procedure in relation to the individual circumstances. This has given rise to many different methods of sampling to cover a wide range of situations.
Peter Kenny

Chapter 5. The Raw Data

Hard to Digest Until Processed
Abstract
Raw data is the expression used to describe the original data before any analysis is undertaken. It is not a very palatable phrase. Something like “original data” or “new data” would have been more inviting, but I have to stick to convention. The purpose of this chapter is to explain the different kinds of data and present a number of definitions to be used in the chapters that follow. In addition, I will demonstrate how figures can mislead or confuse even before the statistical analysis has started.
Peter Kenny

Samples

Frontmatter

Chapter 6. Descriptive Data

Not Every Picture Is Worth a Thousand Words
Abstract
There is not much that can be done to characterize a sample of descriptive data in comparison with the options available for numerical data. The latter has had the advantages of centuries of development of mathematics. Where possible, and usually by simply counting, descriptive data is rendered numerical. In addition, the frequent use of diagrams provides neat summaries of the data, though there are many ways in which diagrams can mislead.
Peter Kenny

Chapter 7. Numerical Data

Abstract
Are Your Statistics Normal?
Peter Kenny

Comparisons

Frontmatter

Chapter 8. Levels of Significance

What Odds Are You Giving?
Abstract
When we obtain two or more samples, we may expect them to be from the same population. Thus we may sample goods produced on two production lines in the same factory, or we could be comparing the same product from two different suppliers. If we find samples to be from the same population, we can pool them to create a larger sample and summarize the data more succinctly. If we find the samples to be from different populations, we are in a position to draw important conclusions. We might change our supplier, for example.
Peter Kenny

Chapter 9. General Procedure for Comparisons

Eight Easy Steps from Null to Significance
Abstract
After you decide what is to be compared with what, you should clearly define the null hypothesis. It is very easy to later become confused between the null hypothesis and the alternative hypothesis.
Peter Kenny

Chapter 10. Comparisons with Numerical Data

Are Today’s Chocolate Bars Smaller Than Yesterday’s?
Abstract
Once a numerical sample or population has been characterized in a quantifiable way, as shown in Chapter 7, it can be compared with others to seek differences or similarities. This chapter explains what can be learned from single values, pairs of values, pairs of samples, and sets of samples. In each case, the null hypothesis, that no difference is evidenced, is set up; and, by calculating the appropriate test statistic, it is established whether the null hypothesis should be accepted or not.
Peter Kenny

Chapter 11. Comparisons with Descriptive Data

Is Your Staff Female/Male Ratio OK?
Abstract
Chapter 6 explained that descriptive data can be rendered numerical by expressing the numbers of items in various categories as proportions—thus enabling further analysis to be carried out on the data. In this chapter, a single proportion will be compared with a population, and two sample proportions will be compared. If the data is ordinal—that is to say, it can be listed in logical order—then ranking tests, which will be introduced, can be applied to achieve comparisons between pairs of ranks.
Peter Kenny

Chapter 12. Types of Error

How Wrong Can You Be?
Abstract
Whenever a significance level is quoted, there is a chance that the stated result is incorrect. If the null hypothesis is rejected when, in fact, it is correct, the error is referred to as a Type I error. So, if our null hypothesis is that there is no significant difference between the mean marks from the boys’ results and the girls’ results in the same examination, we may decide that there is a difference, at the 5% level, say. If in fact there is no difference, and our result is simply due to the random effect embodied in our 1-in-20 chance of being wrong, then a Type I error has occurred.
Peter Kenny

Relationships

Frontmatter

Chapter 13. Cause and Effect

Storks and Birth Rates
Abstract
We human beings seem to have an inbuilt desire to seek out relationships between different observed effects, and deduce a cause-and-effect association. I suppose that survival depends to some extent on recognizing relationships and assuming that one effect causes another. As youngsters we learn of danger by relating climbing to the risk of falling. Crossing the road without looking is related to the possibility of being struck by a vehicle, and so on. However, we are inclined to imagine relationships where none exist, and, worse still, to imagine that these relationships imply cause and effect. The extreme situation is in the area of superstition: a remarkably high percentage of the population avoid the number thirteen or carry lucky charms. Astrology, which claims that events in our lives are affected by the positions of the planets, has a large following.
Peter Kenny

Chapter 14. Relationships with Numerical Data

Straight Lines, Curved Lines, and Wiggly Lines
Abstract
It is frequently required to compare two or more sets of data to decide whether they are related in some way. Some quantities are related because we have defined them to be so. Kilometers are related to miles in a precise way and the relationship can be expressed as a formula:
Peter Kenny

Chapter 15. Relationships with Descriptive Data

Any Color as Long as It’s Black
Abstract
Much of the data involved in business operations is descriptive rather than numerical. In product development and marketing we have decisions to make regarding color, shape and packaging. Surveys will have resulted in yes/no answers to questions. Records will show whether a product is popular or unpopular, whether it sells or doesn't sell.
Peter Kenny

Chapter 16. Multivariate Data

Variety Is the Spice of Life
Abstract
Practical problems often make it difficult to obtain homogeneous and similar samples. For example, samples may involve individuals of different ages and may have to be taken on different days of the week. Individuals differ in numerous ways, and real effects can arise on different days. It could be said, quite rightly, that samples differ because a variety of effects are always present, each creating a difference. In other words, no matter how we aim to obtain homogeneous samples, we will end up with multiple effects. In the past, when analysis involved lengthy procedures, this was a nuisance. Now, with the availability of computer packages that provide rapid and more versatile processing, multivariate data analysis is seen to be a great advantage and has in many areas taken over from the simplistic methods I have been describing.
Peter Kenny

Forecasts

Frontmatter

Chapter 17. Extrapolation

Malthus Got It Wrong
Abstract
Thomas Malthus, an English cleric, economist, and statistician, is known for his theories on population growth. He wrote in 1798 in his Essay on the Principle of Population that, because population increases geometrically (1, 2, 4, 8, …) and food increases arithmetically (1, 2, 3, 4, …), the population would eventually outstrip food supply. He warned of premature death visiting the human race. The onset of disaster would be prevented only by epidemics, pestilence, plague, famine, and preventive measures. His numerous writings on the subject gave rise to the Malthusian doctrine.
Peter Kenny

Chapter 18. Forecasting from Known Distributions

Why Does the Phone Never Stop Ringing?
Abstract
The normal distribution has featured prominently in previous chapters because it is found to appropriately describe the data obtained in numerous situations. If there is good reason to believe in advance that the normal distribution will apply, then predictions can be made regarding future observations. Many other distributions are found to apply in certain circumstances, and, in a similar way, these can provide useful estimates of future outcomes. This chapter describes several of the commonly used distributions and gives examples of their use in forecasting.
Peter Kenny

Chapter 19. Time Series

Yesterday Rain, Today Rain, Tomorrow...?
Abstract
One of the most difficult areas of forecasting is in dealing with time series. Our profits have been x, y, and z over the past three years, so what will they be next year? Unfortunately, we might say, this is perhaps the area where forecasting is most necessary in the business and commercial world. In the United Kingdom, and probably elsewhere, documentation involving the sale of shares and other investment products has to carry the warning “past performance is no guide to future performance.”
Peter Kenny

Chapter 20. Control Charts

Navigating around the Factory
Abstract
Quality control procedures are used in production processes to ensure that the products continue to meet the appropriate specifications. Usually, periodic sampling of the products is employed; and control charts, sometimes referred to as Shewhart charts, are used to record the results in order to anticipate the onset of problems in the production processes.
Peter Kenny

Chapter 21. Reliability

Would You Trust That Bungee Cord?
Abstract
Statistics plays an important part in reliability studies but represents only a part of the mathematical theory involved. Reliability of a component, machine, or system can be defined as the probability that it will perform its required function in the desired manner under the operating conditions when it is required to so perform. Reliability, R, is thus a probability with a value between 0 and 1, 0 representing immediate failure and 1 representing the (impossible) situation of never suffering failure. The probability of failure is 1 – R.
Peter Kenny

Big Data

Frontmatter

Chapter 22. Data Mining

Twenty-First-Century Gold Rush
Abstract
Data mining is a means of producing predictive information from large amounts of data. It is one of the fastest growing methods of forecasting in the business world and is exciting in the prospects it offers for the future.
Peter Kenny

Chapter 23. Predictive Analytics

It’s Only Arithmetic!
Abstract
The first step in interrogating the data for a possible relationship is the selection of a limited amount of data, called the training data, from which a model will be developed. The model is an idealized relationship, involving a number of variables, that is suggested by initial examination of the training data or by practical observations. Many different kinds of models are in use, having been drawn from different disciplines. Predictive analytics is essentially a statistical process in that the results obtained are not precise but are expressed in terms of probability. Thus, levels of reliability in terms of confidence limits are a feature. The various statistical methods that we have discussed in previous chapters have their use in setting up proposed models. In addition, techniques from studies of machine learning, artificial intelligence, and neural networks are in use. The development of new and improved models is an active area of research. The following sections are intended to give an indication of the kinds of models that are used and the way in which they work.
Peter Kenny

Chapter 24. Getting Involved with Big Data

What Would You Like To Know?
Abstract
In the previous chapter, we saw that the procedures for extracting information from big data can be quite simple. In contrast, the setting up of computer systems for applying the procedures to large amounts of data in a reliable and rapid manner requires considerable expertise. Following a summary of the potential applications of big data, we will discuss how businesses can become part of this new and exciting development.
Peter Kenny

Chapter 25. Concerns with Big Data

The Small Print
Abstract
Beneficial innovations always have downsides. We accept large numbers of deaths from road accidents and occasional air disasters for the benefits of faster travel. We accept the risk of nuclear war for the benefits of nuclear energy. Big data is not unique in having problem areas—but we are not contemplating the end of civilization!
Peter Kenny

APPENDIX 26. References and Further Reading

Abstract
Blastland, Michael and Andrew Dilnot. 2007. The Tiger That Isn’t: Seeing through a World of Numbers. London: Profile Books.
Peter Kenny

Backmatter

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