ISE Statistical Techniques in Business and Economics

Höfundar: Douglas Lind; William Marchal; Samuel Wathen (Útgáfa: 19)
ISE Statistical Techniques in Business and Economics

Kaup valmöguleikar

From its origins in 1976, Statistical Techniques in Business and Economics has become a best seller, providing students across the business specialisms with an introductory survey of descriptive and inferential statistics. With a step-by-step approach and clear writing style, any student can learn and succeed in Business Statistics. The new edition includes a focus on the development of basic data analytical skills.

Microsoft Excel, Minitab, and MegaStat are used throughout the text to illustrate statistical software analyses, and screen captures are used within the chapters, so the student becomes familiar with the nature of the software output. Additional resources are available online through Connect, with references or links in the text to Excel tutorials that provide users with clear demonstrations using statistical software to create graphical and descriptive statistics and analyses to test hypotheses.

What's changed: The Nineteenth Edition made several changes to the flow and organization of the text by adding the sampling distribution of the proportion to Chapter 8, moving the one- and two-sample tests of hypothesis for proportions to Chapters 10, preceding the two-sample tests of hypothesis in Chapter 11 with the F-distribution, and the revision of several Chapter introductions. Many of the calculation examples have been replaced with interpretative ones to help students understand and interpret the statistical results with more emphasis on the conceptual nature of the topics.

Nánar um bókina

Útgefandi
McGraw-Hill Higher Education (International)
ISBN
9781266452277
Print ISBN
9781266283536
Format
ePub
Útgáfa
19
Höfundar
Douglas Lind; William Marchal; Samuel Wathen
Tungumál
English
Útgefið
2023-01-03
Prent takmörkun á líftíma
100
Flokkar
Hagfræði

Kaflar

  • Cover
  • Half-Title Page
  • Series Page
  • Title Page
  • Copyright Page
  • Dedication
  • A Note from the Authors
  • How are Chapters Organized to Engage Students and Promote Learning?
  • How Does this Text Reinforce Student Learning?
  • Connect
  • Additional Resources
  • Acknowledgments
  • Enhancements to Statistical Techniques in Business & Economics, 19E
  • Brief Contents
  • Contents
  • 1 What Is Statistics?
  • Introduction
  • Why Study Statistics?
  • What Is Meant by Statistics?
  • Types of Statistics
  • Descriptive Statistics
  • Inferential Statistics
  • Types of Variables
  • Levels of Measurement
  • Nominal-Level Data
  • Ordinal-Level Data
  • Interval-Level Data
  • Ratio-Level Data
  • Exercises
  • Ethics and Statistics
  • Basic Business Analytics
  • Chapter Summary
  • Chapter Exercises
  • Data Analytics
  • 2 Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation
  • Introduction
  • Constructing Frequency Tables
  • Relative Class Frequencies
  • Graphic Presentation of Qualitative Data
  • Exercises
  • Constructing Frequency Distributions
  • Relative Frequency Distribution
  • Exercises
  • Graphic Presentation of a Distribution
  • Histogram
  • Frequency Polygon
  • Exercises
  • Cumulative Distributions
  • Exercises
  • Chapter Summary
  • Chapter Exercises
  • Data Analytics
  • 3 Describing Data: Numerical Measures
  • Introduction
  • Measures of Location
  • The Population Mean
  • The Sample Mean
  • Properties of the Arithmetic Mean
  • Exercises
  • The Median
  • The Mode
  • Software Solution
  • Exercises
  • The Relative Positions of the Mean, Median, and Mode
  • Exercises
  • The Weighted Mean
  • Exercises
  • The Geometric Mean
  • Exercises
  • Why Study Dispersion?
  • Range
  • Variance
  • Exercises
  • Population Variance
  • Population Standard Deviation
  • Exercises
  • Sample Variance and Standard Deviation
  • Software Solution
  • Exercises
  • Interpretation and Uses of the Standard Deviation
  • Chebyshev’s Theorem
  • The Empirical Rule
  • Exercises
  • Ethics and Reporting Results
  • Chapter Summary
  • Pronunciation Key
  • Chapter Exercises
  • Data Analytics
  • 4 Describing Data: Displaying and Exploring Data
  • Introduction
  • Dot Plots
  • Exercises
  • Measures of Position
  • Exercises
  • Box Plots
  • Exercises
  • Skewness
  • Exercises
  • Describing the Relationship between Two Variables
  • Correlation Coefficient
  • Contingency Tables
  • Exercises
  • Chapter Summary
  • Pronunciation Key
  • Chapter Exercises
  • Data Analytics
  • A Review of Chapters 1–4
  • Problems
  • Cases
  • Practice Test
  • 5 A Survey of Probability Concepts
  • Introduction
  • What Is a Probability?
  • Approaches to Assigning Probabilities
  • Classical Probability
  • Exercises
  • Empirical Probability
  • Subjective Probability
  • Exercises
  • Rules of Addition for Computing Probabilities
  • Special Rule of Addition
  • Complement Rule
  • The General Rule of Addition
  • Exercises
  • Rules of Multiplication to Calculate Probability
  • Special Rule of Multiplication
  • General Rule of Multiplication
  • Contingency Tables
  • Tree Diagrams
  • Exercises
  • Bayes’ Theorem
  • Exercises
  • Chapter Summary
  • Pronunciation Key
  • Chapter Exercises
  • Data Analytics
  • 6 Discrete Probability Distributions
  • Introduction
  • What Is a Probability Distribution?
  • Random Variables
  • Discrete Random Variable
  • Continuous Random Variable
  • The Mean, Variance, and Standard Deviation of a Discrete Probability Distribution
  • Mean
  • Variance and Standard Deviation
  • Exercises
  • Binomial Probability Distribution
  • How Is a Binomial Probability Computed?
  • Binomial Probability Tables
  • Exercises
  • Cumulative Binomial Probability Distributions
  • Exercises
  • Hypergeometric Probability Distribution
  • Exercises
  • Poisson Probability Distribution
  • Exercises
  • Chapter Summary
  • Chapter Exercises
  • Data Analytics
  • 7 Continuous Probability Distributions
  • Introduction
  • The Family of Uniform Probability Distributions
  • Exercises
  • The Family of Normal Probability Distributions
  • The Standard Normal Probability Distribution
  • The Standard Normal Distribution
  • Applications of the Standard Normal Distribution
  • Exercises
  • Exercises
  • Exercises
  • The Empirical Rule
  • Exercises
  • The Family of Exponential Distributions
  • Exercises
  • Chapter Summary
  • Chapter Exercises
  • Data Analytics
  • A Review of Chapters 5–7
  • Problems
  • Cases
  • Practice Test
  • 8 Sampling, Sampling Methods, and the Central Limit Theorem
  • Introduction
  • Research and Sampling
  • Sampling Methods
  • Simple Random Sampling
  • Systematic Random Sampling
  • Stratified Random Sampling
  • Cluster Sampling
  • Exercises
  • Sample Mean as a Random Variable
  • Sampling Distribution of the Sample Mean
  • Exercises
  • The Central Limit Theorem
  • Standard Error of the Mean
  • Exercises
  • Using the Sampling Distribution of the Sample Mean
  • Exercises
  • The Sampling Distribution of the Sample Proportion
  • Exercises
  • Chapter Summary
  • Pronunciation Key
  • Chapter Exercises
  • Data Analytics
  • 9 Estimation and Confidence Intervals
  • Introduction
  • Point Estimate for a Population Mean
  • Confidence Intervals for a Population Mean
  • Population Standard Deviation, Known σ
  • A Computer Simulation
  • Exercises
  • Population Standard Deviation, σ Unknown
  • Exercises
  • A Confidence Interval for a Population Proportion
  • Exercises
  • Finite-Population Correction Factor
  • Exercises
  • Choosing an Appropriate Sample Size
  • Sample Size to Estimate a Population Mean
  • Sample Size to Estimate a Population Proportion
  • Exercises
  • Chapter Summary
  • Chapter Exercises
  • Data Analytics
  • A Review of Chapters 8–9
  • Problems
  • Cases
  • Practice Test
  • 10 One-Sample Tests of Hypothesis
  • Introduction
  • What Is Hypothesis Testing?
  • Six-Step Procedure for Testing a Hypothesis
  • Step 1: State the Null Hypothesis (H0) and the Alternate Hypothesis (H1)
  • Step 2: Select a Level of Significance
  • Step 3: Select the Test Statistic
  • Step 4: Formulate the Decision Rule
  • Step 5: Make a Decision
  • Step 6: Interpret the Result
  • One-Tailed and Two-Tailed Hypothesis Tests
  • Hypothesis Testing for a Population Mean: Known Population Standard Deviation
  • A Two-Tailed Test
  • A One-Tailed Test
  • p-Value in Hypothesis Testing
  • Exercises
  • Hypothesis Testing for a Population Mean: Population Standard Deviation Unknown
  • Exercises
  • A Statistical Software Solution
  • Exercises
  • Type II Error
  • Exercises
  • Test a Hypothesis of a Population Proportion
  • Exercises
  • Chapter Summary
  • Pronunciation Key
  • Chapter Exercises
  • Data Analytics
  • 11 Two-Sample Tests of Hypothesis
  • Introduction
  • Comparing Two Population Variances
  • The F-Distribution
  • Testing a Hypothesis of Equal Population Variances
  • Exercises
  • Two-Sample Tests of Hypothesis: Independent Samples
  • Exercises
  • Comparing Population Means with Estimated Population Standard Deviations
  • Two-Sample Pooled Test
  • Exercises
  • Unequal Population Standard Deviations
  • Exercises
  • Two-Sample Tests of Hypothesis: Dependent Samples
  • Comparing Dependent and Independent Samples
  • Exercises
  • Two-Sample Tests about Proportions
  • Exercises
  • Chapter Summary
  • Pronunciation Key
  • Chapter Exercises
  • Data Analytics
  • 12 Analysis of Variance
  • Introduction
  • ANOVA: Analysis of Variance
  • ANOVA Assumptions
  • The ANOVA Test
  • Exercises
  • Inferences about Pairs of Treatment Means
  • Exercises
  • Two-Way Analysis of Variance
  • Exercises
  • Two-Way ANOVA with Interaction
  • Interaction Plots
  • Testing for Interaction
  • Hypothesis Tests for Interaction
  • Exercises
  • Chapter Summary
  • Pronunciation Key
  • Chapter Exercises
  • Data Analytics
  • A Review of Chapters 10–12
  • Problems
  • Cases
  • Practice Test
  • 13 Correlation and Linear Regression
  • Introduction
  • What Is Correlation Analysis?
  • The Correlation Coefficient
  • Exercises
  • Testing the Significance of the Correlation Coefficient
  • Exercises
  • Regression Analysis
  • Least Squares Principle
  • Drawing the Regression Line
  • Exercises
  • Testing the Significance of the Slope
  • Exercises
  • Evaluating a Regression Equation’s Ability to Predict
  • The Standard Error of Estimate
  • The Coefficient of Determination
  • Exercises
  • Relationships among the Correlation Coefficient, the Coefficient of Determination, and the Standard Error of Estimate
  • Exercises
  • Interval Estimates of Prediction
  • Assumptions Underlying Linear Regression
  • Constructing Confidence and Prediction Intervals
  • Exercises
  • Transforming Data
  • Exercises
  • Chapter Summary
  • Pronunciation Key
  • Chapter Exercises
  • Data Analytics
  • 14 Multiple Regression Analysis
  • Introduction
  • Multiple Regression Analysis
  • Exercises
  • Evaluating a Multiple Regression Equation
  • The ANOVA Table
  • Multiple Standard Error of Estimate
  • Coefficient of Multiple Determination
  • Adjusted Coefficient of Determination
  • Exercises
  • Inferences in Multiple Linear Regression
  • Global Test: Testing the Multiple Regression Model
  • Evaluating Individual Regression Coefficients
  • Exercises
  • Evaluating the Assumptions of Multiple Regression
  • Linear Relationship
  • Variation in Residuals Same for Large and Small ŷ Values
  • Distribution of Residuals
  • Multicollinearity
  • Independent Observations
  • Qualitative Independent Variables
  • Regression Models with Interaction
  • Stepwise Regression
  • Exercises
  • Review of Multiple Regression
  • Chapter Summary
  • Pronunciation Key
  • Chapter Exercises
  • Data Analytics
  • A Review of Chapters 13–14
  • Problems
  • Cases
  • Practice Test
  • 15 Nonparametric Methods Nominal Level Hypothesis Tests
  • Introduction
  • Goodness-of-Fit Tests: Comparing Observed and Expected Frequency Distributions
  • Hypothesis Test of Equal Expected Frequencies
  • Exercises
  • Hypothesis Test of Unequal Expected Frequencies
  • Limitations of Chi-Square
  • Exercises
  • Testing the Hypothesis That a Distribution Is Normal
  • Exercises
  • Contingency Table Analysis
  • Exercises
  • Chapter Summary
  • Pronunciation Key
  • Chapter Exercises
  • Data Analytics
  • 16 Nonparametric Methods: Analysis of Ordinal Data
  • Introduction
  • The Sign Test
  • Exercises
  • Testing a Hypothesis About a Median
  • Exercises
  • Wilcoxon Signed-Rank Test for Dependent Populations
  • Exercises
  • Wilcoxon Rank-Sum Test for Independent Populations
  • Exercises
  • Kruskal-Wallis Test: Analysis of Variance by Ranks
  • Exercises
  • Rank-Order Correlation
  • Testing the Significance of rs
  • Exercises
  • Chapter Summary
  • Pronunciation Key
  • Chapter Exercises
  • Data Analytics
  • A Review of Chapters 15–16
  • Problems
  • Cases
  • Practice Test
  • 17 Index Numbers
  • Introduction
  • Simple Index Numbers
  • Why Convert Data to Indexes?
  • Construction of Index Numbers
  • Exercises
  • Unweighted Indexes
  • Simple Average of the Price Indexes
  • Simple Aggregate Index
  • Weighted Indexes
  • Laspeyres Price Index
  • Paasche Price Index
  • Fisher’s Ideal Index
  • Exercises
  • Value Index
  • Exercises
  • Special-Purpose Indexes
  • Consumer Price Index
  • Producer Price Index
  • Dow Jones Industrial Average (DJIA)
  • Exercises
  • Consumer Price Index
  • Special Uses of the Consumer Price Index
  • Shifting the Base
  • Exercises
  • Chapter Summary
  • Chapter Exercises
  • Data Analytics
  • 18 Forecasting with Time Series Analysis
  • Introduction
  • Time Series Patterns
  • Trend
  • Seasonality
  • Cycles
  • Irregular Component
  • Exercises
  • Modeling Stationary Time Series: Forecasts Using Simple Moving Averages
  • Forecasting Error
  • Exercises
  • Modeling Stationary Time Series: Simple Exponential Smoothing
  • Exercises
  • Modeling Time Series with Trend: Regression Analysis
  • Regression Analysis
  • Exercises
  • The Durbin-Watson Statistic
  • Exercises
  • Modeling Time Series with Seasonality: Seasonal Indexing
  • Exercises
  • Chapter Summary
  • Chapter Exercises
  • Data Analytics
  • A Review of Chapters 17–18
  • Practice Test
  • Problems
  • 19 Statistical Process Control and Quality Management
  • Introduction
  • A Brief History of Quality Control
  • Six Sigma
  • Sources of Variation
  • Diagnostic Charts
  • Pareto Charts
  • Fishbone Diagrams
  • Exercises
  • Purpose and Types of Quality Control Charts
  • Control Charts for Variables
  • Range Charts
  • In-Control and Out-of-Control Situations
  • Exercises
  • Attribute Control Charts
  • p-Charts
  • c-Bar Charts
  • Exercises
  • Acceptance Sampling
  • Exercises
  • Chapter Summary
  • Pronunciation Key
  • Chapter Exercises
  • Appendixes
  • Appendix A: Data Sets
  • Appendix B: Tables
  • Appendix C: Answers to Odd-Numbered Chapter Exercises
  • Review Exercises
  • Solutions to Practice Tests
  • Appendix D: Answers to Self-Review
  • Glossary
  • Index
  • Key Formulas
  • Student’s t-Distribution
  • Areas Under the Normal Curve