## Specifications

book-author | Amir D. Aczel |
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publisher | Irwin Professional Pub; 7th edition |
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file-type | PDF |
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pages | 888 pages |
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language | English |
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isbn10 | 0073373605 / 0071284931 |
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isbn13 | 9780073373607 / 9780071284936 |
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## Book Description

This *Complete Business Statistics 7th edition (PDF)* aims to provide college students with a solid understanding of statistical concepts. It also exposes business students to the more contemporary uses of technology in business statistics. This ebook features: Excel output in textbook examples; to give students a foundation for learning and applying statistics with commercial software tools; a visual statistics software bundle that teaches statistics using multimedia enhanced visuals to help students understand business statistics concepts; and examples; exercises; problems and cases which offer students examples drawn from real business situations. The main aim of this etextbook is to offer business tudents a realistic understanding of statistics and how they are applied in business.

## Table of contents

Table of contents :

Title

Contents

1 Introduction and Descriptive Statistics

1–1 Using Statistics

Samples and Populations

Data and Data Collection

1–2 Percentiles and Quartiles

1–3 Measures of Central Tendency

1–4 Measures of Variability

1–5 Grouped Data and the Histogram

1–6 Skewness and Kurtosis

1–7 Relations between the Mean and the Standard Deviation

Chebyshev’s Theorem

The Empirical Rule

1–8 Methods of Displaying Data

Pie Charts

Bar Charts

Frequency Polygons and Ogives

A Caution about Graphs

Time Plots

1–9 Exploratory Data Analysis

Stem-and-Leaf Displays

Box Plots

1–10 Using the Computer

Using Excel for Descriptive Statistics and Plots

Using MINITAB for Descriptive Statistics and Plots

1–11 Summary and Review of Terms

Case 1: NASDAQ Volatility

2 Probability

2–1 Using Statistics

2–2 Basic Definitions: Events, Sample Space, and Probabilities

2–3 Basic Rules for Probability

The Range of Values

The Rule of Complements

Mutually Exclusive Events

2–4 Conditional Probability

2–5 Independence of Events

Product Rules for Independent Events

2–6 Combinatorial Concepts

2–7 The Law of Total Probability and Bayes’ Theorem

The Law of Total Probability

Bayes’ Theorem

Case 2: Job Applications

2–8 The Joint Probability Table

2–9 Using the Computer

Excel Templates and Formulas

Using MINITAB

2–10 Summary and Review of Terms

3 Random Variables

3–1 Using Statistics

Discrete and Continuous Random Variables

Cumulative Distribution Function

3–2 Expected Values of Discrete Random Variables

The Expected Value of a Function of a Random Variable

Variance and Standard Deviation of a Random Variable

Variance of a Linear Function of a Random Variable

3–3 Sum and Linear Composites of Random Variables

Chebyshev’s Theorem

The Templates for Random Variables

3–4 Bernoulli Random Variable

3–5 The Binomial Random Variable

Conditions for a Binomial Random Variable

Binomial Distribution Formulas

The Template

Problem Solving with the Template

3–6 Negative Binomial Distribution

Negative Binomial Distribution Formulas

Problem Solving with the Template

3–7 The Geometric Distribution

Geometric Distribution Formulas

Problem Solving with the Template

3–8 The Hypergeometric Distribution

Hypergeometric Distribution Formulas

Problem Solving with the Template

3–9 The Poisson Distribution

Problem Solving with the Template

3–10 Continuous Random Variables

3–11 The Uniform Distribution

Problem Solving with the Template

3–12 The Exponential Distribution

A Remarkable Property

3–14 Summary and Review of Terms

The Template

Value at Risk

3–13 Using the Computer

Using Excel Formulas for Some Standard Distributions

Using MINITAB for Some Standard Distributions

Case 3: Concepts Testing

4 The Normal Distribution

4–1 Using Statistics

4–2 Properties of the Normal Distribution

4–3 The Standard Normal Distribution

Finding Probabilities of the Standard Normal Distribution

Finding Values of Z Given a Probability

4–4 The Transformation of Normal Random Variables

Using the Normal Transformation

4–5 The Inverse Transformation

4–6 The Template

Problem Solving with the Template

4–7 Normal Approximation of Binomial Distributions

4–8 Using the Computer

Using Excel Functions for a Normal Distribution

Using MINITAB for a Normal Distribution

4–9 Summary and Review of Terms

Case 4: Acceptable Pins

Case 5: Multicurrency Decision

5 Sampling and Sampling Distributions

5–1 Using Statistics

5–2 Sample Statistics as Estimators of Population Parameters

Obtaining a Random Sample

Other Sampling Methods

Nonresponse

5–3 Sampling Distributions

The Central Limit Theorem

The History of the Central Limit Theorem

The Standardized Sampling Distribution of the Sample Mean When � Is Not Known

The Sampling Distribution of the Sample Proportion ˆ P

5–4 Estimators and Their Properties

Applying the Concepts of Unbiasedness, Efficiency, Consistency, and Sufficiency

5–5 Degrees of Freedom

5–6 Using the Computer

Using Excel for Generating Sampling Distributions

Using MINITAB for Generating Sampling Distributions

5–7 Summary and Review of Terms

Case 6: Acceptance Sampling of Pins

Case 9: Tiresome Tires I

6 Confidence Intervals

6–1 Using Statistics

6–2 Confidence Interval for the Population Mean When the Population Standard Deviation Is Known

The Template

6–3 Confidence Intervals for � When � Is Unknown— The t Distribution

The t Distribution

6–4 Large-Sample Confidence Intervals for the Population Proportion p

The Template

6–5 Confidence Intervals for the Population Variance

The Template

6–6 Sample-Size Determination

6–7 The Templates

Optimizing Population Mean Estimates

Determining the Optimal Half-Width

Using the Solver

Optimizing Population Proportion Estimates

6–8 Using the Computer

Using Excel Built-In Functions for Confidence Interval Estimation

Using MINITAB for Confidence Interval Estimation

6–9 Summary and Review of Terms

Case 7: Presidential Polling

Case 8: Privacy Problem

7 Hypothesis Testing

7–1 Using Statistics

The Null Hypothesis

7–2 The Concepts of Hypothesis Testing

Evidence Gathering

Type I and Type II Errors

The p-Value

The Significance Level

Optimal � and the Compromise between Type I and Type II Errors

� and Power

Sample Size

7–3 Computing the p-Value

The Test Statistic

p-Value Calculations

One-Tailed and Two-Tailed Tests

Computing �

7–4 The Hypothesis Test

Testing Population Means

A Note on t Tables and p-Values

The Templates

Testing Population Proportions

Testing Population Variances

7–5 Pretest Decisions

Testing Population Means

Manual Calculation of Required Sample Size

Testing Population Proportions

Manual Calculation of Sample Size

7–6 Using the Computer

Using Excel for One-Sample Hypothesis Testing

Using MINITAB for One-Sample Hypothesis Testing

7–7 Summary and Review of Terms

8 The Comparison of Two Populations

8–1 Using Statistics

8–2 Paired-Observation Comparisons

The Template

Confidence Intervals

The Template

8–3 A Test for the Difference between Two Population Means Using Independent Random Samples

The Templates

Confidence Intervals

The Templates

Confidence Intervals

8–4 A Large-Sample Test for the Difference between Two Population Proportions

Confidence Intervals

The Template

8–5 The F Distribution and a Test for Equality of Two Population Variances

A Statistical Test for Equality of Two Population Variances

The Templates

8–6 Using the Computer

Using Excel for Comparison of Two Populations

Using MINITAB for Comparison of Two Samples

8–7 Summary and Review of Terms

Case 10: Tiresome Tires II

9 Analysis of Variance

9–1 Using Statistics

9–2 The Hypothesis Test of Analysis of Variance

The Test Statistic

9–3 The Theory and the Computations of ANOVA

The Sum-of-Squares Principle

The Degrees of Freedom

The Mean Squares

The Expected Values of the Statistics MSTR and MSE under the Null Hypothesis

The F Statistic

9–4 The ANOVA Table and Examples

9–5 Further Analysis

The Tukey Pairwise-Comparisons Test

Conducting the Tests

The Case of Unequal Sample Sizes, and Alternative Procedures

The Template

9–6 Models, Factors, and Designs

One-Factor versus Multifactor Models

Fixed-Effects versus Random-Effects Models

Experimental Design

9–7 Two-Way Analysis of Variance

The Two-Way ANOVA Model

The Hypothesis Tests in Two-Way ANOVA

Sums of Squares, Degrees of Freedom, and Mean Squares

The F Ratios and the Two-Way ANOVA Table

The Template

The Overall Significance Level

The Tukey Method for Two-Way Analysis

Extension of ANOVA to Three Factors

Two-Way ANOVA with One Observation per Cell

9–8 Blocking Designs

Randomized Complete Block Design

The Template

9–9 Using the Computer

Using Excel for Analysis of Variance

Using MINITAB for Analysis of Variance

9–10 Summary and Review of Terms

Case 11: Rating Wines

Case 12: Checking Out Checkout

10 Simple Linear Regression and Correlation

10–1 Using Statistics

Model Building

10–2 The Simple Linear Regression Model

10–3 Estimation: The Method of Least Squares

The Template

10–4 Error Variance and the Standard Errors of Regression Estimators

Confidence Intervals for the Regression Parameters

10–5 Correlation

10–6 Hypothesis Tests about the Regression Relationship

Other Tests

10–7 How Good Is the Regression?

10–8 Analysis-of-Variance Table and an F Test of the Regression Model

10–9 Residual Analysis and Checking for Model Inadequacies

A Check for the Equality of Variance of the Errors

Testing for Missing Variables

Detecting a Curvilinear Relationship between Y and X

The Normal Probability Plot

10–10 Use of the Regression Model for Prediction

Point Predictions

Prediction Intervals

A Confidence Interval for the Average Y, Given a Particular Value of X

10–11 Using the Computer

The Excel Solver Method for Regression

The Excel LINEST Function

Using MINITAB for Simple Linear Regression Analysis

10–12 Summary and Review of Terms

Case 13: Firm Leverage and Shareholder Rights

Case 14: Risk and Return

11 Multiple Regression

11–1 Using Statistics

11–2 The k-Variable Multiple Regression Model

The Estimated Regression Relationship

11–3 The F Test of a Multiple Regression Model

11–4 How Good Is the Regression?

11–5 Tests of the Significance of Individual Regression Parameters

11–6 Testing the Validity of the Regression Model

Residual Plots

Standardized Residuals

The Normal Probability Plot

Outliers and Influential Observations

Lack of Fit and Other Problems

11–7 Using the Multiple Regression Model for Prediction

The Template

Setting Recalculation to “Manual” on the Template

11–8 Qualitative Independent Variables

Interactions between Qualitative and Quantitative Variables

11–9 Polynomial Regression

Other Variables and Cross-Product Terms

11–10 Nonlinear Models and Transformations

Variance-Stabilizing Transformations

Regression with Dependent Indicator Variable

11–11 Multicollinearity

Causes of Multicollinearity

Detecting the Existence of Multicollinearity

Solutions to the Multicollinearity Problem

11–12 Residual Autocorrelation and the Durbin-Watson Test

11–13 Partial F Tests and Variable Selection Methods

Partial F Tests

Variable Selection Methods

11–14 Using the Computer

Multiple Regression Using the Solver

LINEST Function for Multiple Regression

Using MINITAB for Multiple Regression

11–15 Summary and Review of Terms

Case 15: Return on Capital for Four Different Sectors

12 Time Series, Forecasting, and Index Numbers

12–1 Using Statistics

12–2 Trend Analysis

12–3 Seasonality and Cyclical Behavior

12–4 The Ratio-to-Moving-Average Method

The Template

The Cyclical Component of the Series

Forecasting a Multiplicative Series

12–5 Exponential Smoothing Methods

The Template

12–6 Index Numbers

The Consumer Price Index

The Template

12–7 Using the Computer

Using Microsoft Excel in Forecasting and Time Series

Using MINITAB in Forecasting and Time Series

12–8 Summary and Review of Terms

Case 16: Auto Parts Sales Forecast

13 Quality Control and Improvement

13–1 Using Statistics

13–2 W. Edwards Deming Instructs

13–3 Statistics and Quality

Deming’s 14 Points

Process Capability

Control Charts

Pareto Diagrams

Six Sigma

Acceptance Sampling

Analysis of Variance and Experimental Design

Taguchi Methods

The Template

13–4 The x Chart

The Template

13–5 The R Chart and the s Chart

The R Chart

The s Chart

13–6 The p Chart

The Template

13–7 The c Chart

The Template

13–8 The x Chart

13–9 Using the Computer

Using MINITAB for Quality Control

13–10 Summary and Review of Terms

Case 17: Quality Control and Improvement at Nashua Corporation

14 Nonparametric Methods and Chi-Square Tests

14–1 Using Statistics

14–2 The Sign Test

14–3 The Runs Test—A Test for Randomness

Large-Sample Properties

The Template

The Wald-Wolfowitz Test

14–4 The Mann-Whitney U Test

The Computational Procedure

14–5 The Wilcoxon Signed-Rank Test

The Paired-Observations Two-Sample Test

Large-Sample Version of the Test

A Test for the Mean or Median of a Single Population

The Template

14–6 The Kruskal-Wallis Test—A Nonparametric Alternative to One-Way ANOVA

The Template

Further Analysis

14–7 The Friedman Test for a Randomized Block Design

The Template

14–8 The Spearman Rank Correlation Coefficient

The Template

14–9 A Chi-Square Test for Goodness of Fit

A Goodness-of-Fit Test for the Multinomial Distribution

The Template

Unequal Probabilities

The Template

14–10 Contingency Table Analysis—A Chi-Square Test for Independence

The Template

14–11 A Chi-Square Test for Equality of Proportions

The Median Test

14–12 Using the Computer

Using MINITAB for Nonparametric Tests

14–13 Summary and Review of Terms

Case 18: The Nine Nations of North America

15 Bayesian Statistics and Decision Analysis

15–1 Using Statistics

15–2 Bayes’ Theorem and Discrete Probability Models

The Template

15–3 Bayes’ Theorem and Continuous Probability Distributions

The Normal Probability Model

Credible Sets

The Template

15–4 The Evaluation of Subjective Probabilities

Assessing a Normal Prior Distribution

15–5 Decision Analysis: An Overview

Actions

Chance Occurrences

Probabilities

Final Outcomes

Additional Information

Decision

15–6 Decision Trees

The Payoff Table

15–7 Handling Additional Information Using Bayes’ Theorem

Determining the Payoffs

Determining the Probabilities

15–8 Utility

A Method of Assessing Utility

15–9 The Value of Information

15–10 Using the Computer

The Template

15–11 Summary and Review of Terms

Case 19: Pizzas ‘R’ Us

Case 20: New Drug Development

A References

B Answers to Most Odd-Numbered Problems

C Statistical Tables