A Guide to Business Statistics

David M. McEvoy

• Publisher: Wiley
• Year: 2018
• Print ISBN: 9781119138358
• eBook ISBN: 9781119138372
• Edition: 1
• Topic: Economics
• Subtopic: Statistics for Business & Economics
• Index: Economics

An accessible text that explains fundamental concepts in business statistics that are often obscured by formulae and mathematical notation

A Guide to Business Statistics offers a practical approach to statistics that covers the fundamental concepts in business and economics. The book maintains the level of rigor of a more conventional textbook in business statistics but uses a more stream­lined and intuitive approach. In short, A Guide to Business Statistics provides clarity to the typical statistics textbook cluttered with notation and formulae.

The author—an expert in the field—offers concise and straightforward explanations to the core principles and techniques in business statistics. The concepts are intro­duced through examples, and the text is designed to be accessible to readers with a variety of backgrounds. To enhance learning, most of the mathematical formulae and notation appears in technical appendices at the end of each chapter. This important resource:

• Offers a comprehensive guide to understanding business statistics targeting business and economics students and professionals
• Introduces the concepts and techniques through concise and intuitive examples
• Focuses on understanding by moving distracting formulae and mathematical notation to appendices
• Offers intuition, insights, humor, and practical advice for students of business statistics
• Features coverage of sampling techniques, descriptive statistics, probability, sampling distributions, confidence intervals, hypothesis tests, and regression

Written for undergraduate business students, business and economics majors, teachers, and practitioners, A Guide to Business Statistics offers an accessible guide to the key concepts and fundamental principles in statistics.

Chapter 1
Types of Data

Steven Wright once joked that “42.7% of all statistics are made up on the spot.”¹ One reason that his quip is effective is because there are good reasons to be suspicious of many of the statistics we encounter every day. Statistics are often reported as hard facts that cannot be argued with. This is not so. Statistics, and the data that the statistics are derived from, are generated by humans. Humans are not infallible and neither are the numbers reported from analyzing the data. As consumers of information, sometimes the statistics we encounter are just simply wrong or even nonsensical. There are examples of peer-reviewed publications reporting 200% reductions in some metric. Even reductions of 12,000% have been reported.² Without even glancing at the data analyzed in these studies, we know that such statistics are nonsense. You cannot decrease anything by more than 100%. Once you lose 100% of stuff, you are out of stuff. We tend to believe assertions when they are based on data. The problem is that we often do not look carefully at what type of data is being analyzed, how the data were gathered, and whether the results are valid. To be an active and informed citizen, you need to understand a bit about how statistics are generated and what they can tell us. It all starts with understanding the type of data being analyzed, which is the focus of this first chapter.

In the broadest terms, statistics is the science of collecting, analyzing, and interpreting data. One branch of statistics is concerned with how to describe and present data in useful ways (descriptive statistics) and the other branch is concerned with how to use samples of data to draw conclusions about unknown characteristics of a larger population (inferential statistics). In either case, the starting point is understanding a bit about data. Often, when students hear the term data or data analysis, they picture some geek crunching through endless columns of numbers in search for answers. The truth is that data are simply organized information. Data does not have to be numeric, and not all numeric data can be treated the same way. One great thing about the modern state of technology and connectivity is that we have access to incredible amounts of interesting, and often peculiar, datasets. For example, you can read the last words of every executed criminal in the state of Texas since 1982.³ Or, if you think that is too morbid, you may be interested in the location, speed, age, and height of amusement park rollercoasters found all over the world.⁴ Perhaps, you want to rank every character on the Simpsons by the number of words they spoke between season 1 and season 26.⁵ The point is that there is so much data available to the public that the possibilities are endless. If you want to get weird, get weird.⁶ You can let your imagination lead you to data, but let this book guide you on how to analyze it.

The important point is to recognize what type of data you are working with because that will dictate the way you analyze it. In this chapter, we consider the taxonomy of different data types. To begin, all data can be broadly classified as either categorical or numerical.

1.1 Categorical Data

Categorical data (also called qualitative data) have values described by words rather than numbers. Examples include gender, occupation, major, and location. Often, categorical data are represented with codes to make it easier to manage and manipulate. For example, a dataset that includes college majors may convert accounting = 1, economics = 2, and...

Table of contents

• Cover
• Title Page
• Copyright
• Dedication
• Table of Contents
• Preface
• Chapter 1: Types of Data
• Chapter 2: Populations and Samples
• Chapter 3: Descriptive Statistics
• Chapter 4: Probability
• Chapter 5: The Normal Distribution
• Chapter 6: Sampling Distributions
• Chapter 7: Confidence Intervals
• Chapter 8: Hypothesis Tests of a Population Mean
• Chapter 9: Hypothesis Tests of Categorical Data
• Chapter 10: Hypothesis Tests Comparing Two Parameters
• Chapter 11: Simple Linear Regression
• Chapter 12: Multiple Regression
• Chapter 13: More Topics in Regression
• Index
• End User License Agreement