Business Statistics For Dummies E-Book

Alan Anderson

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 459

Veröffentlichungsjahr: 2013

Beschreibung

Score higher in your business statistics course? Easy. Business statistics is a common course for business majors and MBA candidates. It examines common data sets and the proper way to use such information when conducting research and producing informational reports such as profit and loss statements, customer satisfaction surveys, and peer comparisons. Business Statistics For Dummies tracks to a typical business statistics course offered at the undergraduate and graduate levels and provides clear, practical explanations of business statistical ideas, techniques, formulas, and calculations, with lots of examples that shows you how these concepts apply to the world of global business and economics. * Shows you how to use statistical data to get an informed and unbiased picture of the market * Serves as an excellent supplement to classroom learning * Helps you score your highest in your Business Statistics course If you're studying business at the university level or you're a professional looking for a desk reference on this complicated topic, Business Statistics For Dummies has you covered.

Leseprobe

Business Statistics For Dummies®

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Published simultaneously in Canada

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Library of Congress Control Number: 2013944875

ISBN 978-1-118-63069-3 (pbk); ISBN 978-1-118-78458-7 (ePub); ISBN 978-1-118-78449-5 (PDF)

Manufactured in the United States of America

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Business Statistics For Dummies®

Visit www.dummies.com/cheatsheet/businessstatistics to view this book's cheat sheet.

Table of Contents

Introduction

About This Book

Foolish Assumptions

Icons Used in This Book

Beyond the Book

Where to Go from Here

Part I: Getting Started with Business Statistics

Chapter 1: The Art and Science of Business Statistics

Representing the Key Properties of Data

Analyzing data with graphs

Defining properties and relationships with numerical measures

Probability: The Foundation of All Statistical Analysis

Random variables

Probability distributions

Using Sampling Techniques and Sampling Distributions

Statistical Inference: Drawing Conclusions from Data

Confidence intervals

Hypothesis testing

Simple regression analysis

Multiple regression analysis

Forecasting techniques

Chapter 2: Pictures Tell the Story: Graphical Representations of Data

Analyzing the Distribution of Data by Class or Category

Frequency distributions for quantitative data

Frequency distribution for qualitative values

Cumulative frequency distributions

Histograms: Getting a Picture of Frequency Distributions

Checking Out Other Useful Graphs

Line graphs: Showing the values of a data series

Pie charts: Showing the composition of a data set

Scatter plots: Showing the relationship between two variables

Chapter 3: Finding a Happy Medium: Identifying the Center of a Data Set

Looking at Methods for Finding the Mean

Arithmetic mean

Geometric mean

Weighted mean

Getting to the Middle of Things: The Median of a Data Set

Comparing the Mean and Median

Determining the relationship between mean and median

Acknowledging the relative advantages and disadvantages of the mean and median

Discovering the Mode: The Most Frequently Repeated Element

Chapter 4: Searching High and Low: Measuring Variation in a Data Set

Determining Variance and Standard Deviation

Finding the sample variance

Finding the sample standard deviation

Calculating population variance and standard deviation

Finding the Relative Position of Data

Percentiles: Dividing everything into hundredths

Quartiles: Dividing everything into fourths

Interquartile range: Identifying the middle 50 percent

Measuring Relative Variation

Coefficient of variation: The spread of a data set relative to the mean

Comparing the relative risks of two portfolios

Chapter 5: Measuring How Data Sets Are Related to Each Other

Understanding Covariance and Correlation

Sample covariance and correlation

Population covariance and correlation coefficient

Comparing correlation and covariance

Interpreting the Correlation Coefficient

Showing the relationship between two variables

Application: Correlation and the benefits of diversification

Part II: Probability Theory and Probability Distributions

Chapter 6: Probability Theory: Measuring the Likelihood of Events

Working with Sets

Membership

Subset

Union

Intersection

Complement

Betting on Uncertain Outcomes

The sample space: Everything that can happen

Event: One possible outcome

Computing probabilities of events

Looking at Types of Probabilities

Unconditional (marginal) probabilities: When events are independent

Joint probabilities: When two things happen at once

Conditional probabilities: When one event depends on another

Determining independence of events

Following the Rules: Computing Probabilities

Addition rule

Complement rule

Multiplication rule

Chapter 7: Probability Distributions and Random Variables

Defining the Role of the Random Variable

Assigning Probabilities to a Random Variable

Calculating the probability distribution

Visualizing probability distribution with a histogram

Characterizing a Probability Distribution with Moments

Understanding the summation operator (Σ)

Expected value

Variance and standard deviation

Chapter 8: The Binomial, Geometric, and Poisson Distributions

Looking at Two Possibilities with the Binomial Distribution

Checking out the binomial distribution

Computing binomial probabilities

Moments of the binomial distribution

Graphing the binomial distribution

Determining the Probability of the Outcome That Occurs First: Geometric Distribution

Computing geometric probabilities

Moments of the geometric distribution

Graphing the geometric distribution

Keeping the Time: The Poisson Distribution

Computing Poisson probabilities

Graphing the Poisson distribution

Chapter 9: The Uniform and Normal Distributions: So Many Possibilities!

Comparing Discrete and Continuous Distributions

Working with the Uniform Distribution

Graphing the uniform distribution

Discovering moments of the uniform distribution

Computing uniform probabilities

Understanding the Normal Distribution

Graphing the normal distribution

Getting to know the standard normal distribution

Computing standard normal probabilities

Computing normal probabilities other than standard normal

Chapter 10: Sampling Techniques and Distributions

Sampling Techniques: Choosing Data from a Population

Probability sampling

Nonprobability sampling

Sampling Distributions

Portraying sampling distributions graphically

Moments of a sampling distribution

The Central Limit Theorem

Converting to a standard normal random variable

Part III: Drawing Conclusions from Samples

Chapter 11: Confidence Intervals and the Student’s t-Distribution

Almost Normal: The Student’s t-Distribution

Properties of the t-distribution

Graphing the t-distribution

Probabilities and the t-table

Point estimates vs. interval estimates

Estimating confidence intervals for the population mean

Chapter 12: Testing Hypotheses about the Population Mean

Applying the Key Steps in Hypothesis Testing for a Single Population Mean

Writing the null hypothesis

Coming up with an alternative hypothesis

Choosing a level of significance

Computing the test statistic

Comparing the critical value(s)

Using the decision rule

Testing Hypotheses About Two Population Means

Writing the null hypothesis for two population means

Defining the alternative hypotheses for two population means

Determining the test statistics for two population means

Working with dependent samples

Chapter 13: Testing Hypotheses about Multiple Population Means

Getting to Know the F-Distribution

Defining an F random variable

Measuring the moments of the F-distribution

Using ANOVA to Test Hypotheses

Writing the null and alternative hypotheses

Choosing the level of significance

Computing the test statistic

Finding the critical values using the F-table

Coming to the decision

Using a spreadsheet

Chapter 14: Testing Hypotheses about the Population Mean

Staying Positive with the Chi-Square Distribution

Representing the chi-square distribution graphically

Defining a chi-square random variable

Checking out the moments of the chi-square distribution

Testing Hypotheses about the Population Variance

Defining what you assume to be true: The null hypothesis

Stating the alternative hypothesis

Choosing the level of significance

Calculating the test statistic

Determining the critical value(s)

Practicing the Goodness of Fit Tests

Comparing a population to the Poisson distribution

Comparing a population to the normal distribution

Testing Hypotheses about the Equality of Two Population Variances

The null hypothesis: Equal variances

The alternative hypothesis: Unequal variances

The test statistic

The critical value(s)

The decision about the equality of two population variances

Part IV: More Advanced Techniques: Regression Analysis and Forecasting

Chapter 15: Simple Regression Analysis

The Fundamental Assumption: Variables Have a Linear Relationship

Defining a linear relationship

Using scatter plots to identify linear relationships

Defining the Population Regression Equation

Estimating the Population Regression Equation

Testing the Estimated Regression Equation

Using the coefficient of determination (R2)

Computing the coefficient of determination

The t-test

Using Statistical Software

Assumptions of Simple Linear Regression

Chapter 16: Multiple Regression Analysis: Two or More Independent Variables

The Fundamental Assumption: Variables Have a Linear Relationship

Estimating a Multiple Regression Equation

Predicting the value of Y

The adjusted coefficient of determination

The F-test: Testing the joint significance of the independent variables

The t-test: Determining the significance of the slope coefficients

Checking for Multicollinearity

Chapter 17: Forecasting Techniques: Looking into the Future

Defining a Time Series

Modeling a Time Series with Regression Analysis

Classifying trends

Estimating the trend

Forecasting a Time Series

Changing with the Seasons: Seasonal Variation

Implementing Smoothing Techniques

Moving averages

Centered moving averages

Exploring Exponential Smoothing

Forecasting with exponential smoothing

Comparing the Forecasts of Different Models

Part V: The Part of Tens

Chapter 18: Ten Common Errors That Arise in Statistical Analysis

Designing Misleading Graphs

Drawing the Wrong Conclusion from a Confidence Interval

Misinterpreting the Results of a Hypothesis Test

Placing Too Much Confidence in the Coefficient of Determination (R2)

Assuming Normality

Thinking Correlation Implies Causality

Drawing Conclusions from a Regression Equation when the Data do not Follow the Assumptions

Including Correlated Variables in a Multiple Regression Equation

Placing Too Much Confidence in Forecasts

Using the Wrong Distribution

Chapter 19: Ten Key Categories of Formulas for Business Statistics

Summary Measures of a Population or a Sample

Probability

Discrete Probability Distributions

Continuous Probability Distributions

Sampling Distributions

Confidence Intervals for the Population Mean

Testing Hypotheses about Population Means

Testing Hypotheses about Population Variances

Using Regression Analysis

Forecasting Techniques

About the Author

Cheat Sheet

Connect with Dummies

Introduction

Have you always been scared to death of statistics? You and just about everyone else! The equations are extremely intimidating, and the terminology sounds so . . . boring.

Why, then, is statistics so important? All business disciplines can be analyzed with statistical principles. Statistics make it possible to analyze real world problems with actual data, so that we can understand if our marketing strategy is really working, or how much a company should charge for its products, or any of a million other practical questions.

Without a formal framework for analyzing these types of situations, it would be impossible to have any confidence in our results. This is where the science of statistics comes in. Far from being an overbearing collection of equations, it is a logical framework for analyzing practical business problems with real-world data.

This book is designed to show you how to apply statistics to practical situations in a step-by-step manner, so that by the time you’re done, you’ll know as much about statistics as people with far more education in this area!

About This Book

All business degrees require at least some statistics courses, and there’s a good reason for that! All business disciplines are empirical by nature, meaning that they need to analyze actual data to be successful. The purpose of this book is to:

Give you the principles on which statistical analysis is based

Provide you with many worked-out examples of these principles so that you can master them

Improve your understanding of the circumstances in which each statistical technique should be used

As a For Dummies title, this book is organized into modules; you can skip around and learn about various statistical techniques in the order that suits you. In cases where the contents of a chapter are based on previous readings, you are guided back to the original material. Along the way, there are many helpful tips and reminders so that you get the most out of each chapter. I explain each equation in great detail, and all key terms are explained in depth. You will also find a summary of key formulas at the back of the book along with important statistical tables.

This book can’t make you an expert in statistics, but provides you with a way of improving your knowledge very quickly so that you can use statistics in practical settings right away.

Foolish Assumptions

I am willing to make the following assumptions about you as the reader of this book:

You need to use the techniques in this book in a practical setting and have little or no previous experience with statistics.

OR

You’re a student who feels overwhelmed by a traditional statistics course and feels the need for more background. You can benefit from seeing more examples of the material; statistics is a science that can be learned through practice!

OR

You’re simply interested in improving your knowledge of this field.

In all of these cases, you’re extremely well motivated and can put as much effort into learning statistics as you need. Congratulations! Your reward for reading this book will be a greater understanding of business statistics.

Icons Used in This Book

The following icons are designed to help you use this book quickly and easily. Be sure to keep an eye out for them.

The Remember icon points to information that’s especially important to remember for exam purposes.

The Tip icon presents information like a memory acronym or some other aid to understanding or remembering material.

When you see this icon, pay special attention. The information that follows may be somewhat difficult, confusing, or harmful.

The Technical Stuff icon is used to indicate detailed information; for some people, it might not be necessary to read or understand.

Beyond the Book

In addition to the informative, clever, and (if I may say so) well-written material you're reading right now, this product also comes with some access-anywhere goodies on the web. No matter how well you know statistics by the end of this book, a little extra information is always helpful. Check out the free Cheat Sheet at www.dummies.com/cheatsheet/businessstatistics to learn more about describing populations and samples, random variables, probability distributions, hypothesis testing, and more.

Where to Go from Here

When you’ve become more adept at statistical analysis, you may want to learn the capabilities of a spreadsheet program such as Excel. You may also want to tackle a full-blown statistical package, such as SPSS or SAS. These will eliminate a great deal of the computational burden, freeing you to concentrate on the analysis of the results.

You may also be interested in obtaining further education in this area. For example, you may want to pursue a graduate degree, such as an MBA (master of business administration.) This is an extremely important credential that will open a large number of doors in the business world. You’ll need your statistical skills in order to earn this degree, since it is heavily used throughout the curriculum.

If you’re not ready for graduate school, you may simply want to explore some college-level statistics courses at your local university. The most important thing is to continue using your statistical skills, as you’ll only become adept at using them through constant practice.

Part I

Getting Started with Business Statistics

Visit www.dummies.com for great Dummies content online.

In this part…

Use histograms to provide a visual of the distribution of elements in a data set. A histogram can show which values occur most frequently, the smallest and largest values, how spread out these values are.

Create graphs that reflect non-numerical data, such as colors, flavors, brand names, and so on. Graphs are used where numerical measures are difficult or impossible to compute.

Identify the center of a data set by using the mean (the average), median (the middle), and mode (the most commonly occurring value). These are known as the measures of central tendencies.

Use formulas for computing covariance and correlation for both samples and populations; a scatter plot is used to show the relationship (if there is one) between two variables.

Chapter 1

The Art and Science of Business Statistics

In This Chapter

Looking at the key properties of data

Understanding probability’s role in business

Sampling distributions

Drawing conclusions based on results

This chapter provides a brief introduction to the concepts that are covered throughout the book. I introduce several important techniques that allow you to measure and analyze the statistical properties of real-world variables, such as stock prices, interest rates, corporate profits, and so on.

Statistical analysis is widely used in all business disciplines. For example, marketing researchers analyze consumer spending patterns in order to properly plan new advertising campaigns. Organizations use management consulting to determine how efficiently resources are being used. Manufacturers use quality control methods to ensure the consistency of the products they are producing. These types of business applications and many others are heavily based on statistical analysis.

Financial institutions use statistics for a wide variety of applications. For example, a pension fund may use statistics to identify the types of securities that it should hold in its investment portfolio. A hedge fund may use statistics to identify profitable trading opportunities. An investment bank may forecast the future state of the economy in order to determine which new assets it should hold in its own portfolio.

Whereas statistics is a quantitative discipline, the ultimate objective of statistical analysis is to explain real-world events. This means that in addition to the rigorous application of statistical methods, there is always a great deal of room for judgment. As a result, you can think of statistical analysis as both a science and an art; the art comes from choosing the appropriate statistical technique for a given situation and correctly interpreting the results.

Representing the Key Properties of Data

The word data refers to a collection of quantitative (numerical) or qualitative (non-numerical) values. Quantitative data may consist of prices, profits, sales, or any variable that can be measured on a numerical scale. Qualitative data may consist of colors, brand names, geographic locations, and so on. Most of the data encountered in business applications are quantitative.

The word data is actually the plural of datum; datum refers to a single value, while data refers to a collection of values.

You can analyze data with graphical techniques or numerical measures. I explore both options in the following sections.

Analyzing data with graphs

Graphs are a visual representation of a data set, making it easy to see patterns and other details. Deciding which type of graph to use depends on the type of data you’re trying to analyze. Here are some of the more common types of graphs used in business statistics: