# Statistical techniques in business & economics

Douglas A. Lind (Author), William G. Marchal (Author), Samuel Adam Wathen (Author)
Includes a coverage of statistical concepts and methods delivered in a student friendly, step-by-step format. This book presents concepts clearly and succinctly with a conversational writing style and illustrates concepts through the liberal use of business-focused examples that are relevant to the current world of a college student.
Print Book, English, 2018
Seventeenth edition View all formats and editions
McGraw-Hill Education, New York, NY, 2018
xxvi, 863 pages ; 28 cm.
9781259666360, 9781259921803, 1259666360, 1259921808
958357160
Machine generated contents note: 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
Summary
Exercises
Data Analytics
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
Summary
Exercises
Data Analytics
Introduction
Measures of Location
The Population Mean
The Sample Mean
Properties of the Arithmetic Mean
Exercises
The Median
The Mode
Exercises
The Relative Positions of the Mean, Median, and Mode
Exercises
Software Solution
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
The Mean and Standard Deviation of Grouped Data
Arithmetic Mean of Grouped Data
Standard Deviation of Grouped Data
Exercises
Ethics and Reporting Results
Summary
Pronunciation Key
Exercises
Data Analytics
Introduction
Dot Plots
Stem-and-Leaf Displays
Exercises
Measures of Position
Quartiles, Deciles, and Percentiles
Exercises
Box Plots
Exercises
Skewness
Exercises
Describing the Relationship between Two Variables
Contingency Tables
Exercises
Summary
Pronunciation Key
Exercises
Data Analytics
Problems
Cases
Practice Test
Introduction
What is a Probability?
Approaches to Assigning Probabilities
Classical Probability
Empirical Probability
Subjective Probability
Exercises
Rules of Addition for Computing Probabilities
Complement Rule
Exercises
Rules of Multiplication to Calculate Probability
Special Rule of Multiplication
General Rule of Multiplication
Contingency Tables
Tree Diagrams
Exercises
Bayes' Theorem
Exercises
Principles of Counting
The Multiplication Formula
The Permutation Formula
The Combination Formula
Exercises
Summary
Pronunciation Key
Exercises
Data Analytics
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
Summary
Exercises
Data Analytics
Introduction
The Family of Uniform Probability Distributions
Exercises
The Family of Normal Probability Distributions
The Standard Normal Probability Distribution
Applications of the Standard Normal Distribution
The Empirical Rule
Exercises
Finding Areas under the Normal Curve
Exercises
Exercises
Exercises
The Normal Approximation to the Binomial
Continuity Correction Factor
How to Apply the Correction Factor
Exercises
The Family of Exponential Distributions
Exercises
Summary
Exercises
Data Analytics
Problems
Cases
Practice Test
Introduction
Sampling Methods
Reasons to Sample
Simple Random Sampling
Systematic Random Sampling
Stratified Random Sampling
Cluster Sampling
Exercises
Sampling "Error"
Sampling Distribution of the Sample Mean
Exercises
The Central Limit Theorem
Exercises
Using the Sampling Distribution of the Sample Mean
Exercises
Summary
Pronunciation Key
Exercises
Data Analytics
Introduction
Point Estimate for a Population Mean
Confidence Intervals for a Population Mean
Population Standard Deviation, Known a
A Computer Simulation
Exercises
Population Standard Deviation, a Unknown
Exercises
A Confidence Interval for a Population Proportion
Exercises
Choosing an Appropriate Sample Size
Sample Size to Estimate a Population Mean
Sample Size to Estimate a Population Proportion
Exercises
Finite-Population Correction Factor
Exercises
Summary
Exercises
Data Analytics
Problems
Cases
Practice Test
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
Summary
Pronunciation Key
Exercises
Data Analytics
Introduction
Two-Sample Tests of Hypothesis: Independent Samples
Exercises
Comparing Population Means with Unknown 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
Summary
Pronunciation Key
Exercises
Data Analytics
Introduction
Comparing Two Population Variances
The F Distribution
Testing a Hypothesis of Equal Population Variances
Exercises
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
Summary
Pronunciation Key
Exercises
Data Analytics
Problems
Cases
Practice Test
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
Summary
Pronunciation Key
Exercises
Data Analytics
Introduction
Multiple Regression Analysis
Exercises
Evaluating a Multiple Regression Equation
The ANOVA Table
Multiple Standard Error of Estimate
Coefficient of Multiple 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 Si Values
Distribution of Residuals
Multicollinearity
Independent Observations
Qualitative Independent Variables
Regression Models with Interaction
Stepwise Regression
Exercises
Review of Multiple Regression
Summary
Pronunciation Key
Exercises
Data Analytics
Problems
Cases
Practice Test
Introduction
Test a Hypothesis of a Population Proportion
Exercises
Exercises
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
Summary
Pronunciation Key
Exercises
Data Analytics
Introduction
The Sign Test
Exercises
Using the Normal Approximation to the Binomial
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
Summary
Pronunciation Key
Exercises
Data Analytics
Problems
Cases
Practice Test
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 Note continued: Dow Jones Industrial Average (DJIA)
Exercises
Consumer Price Index
Special Uses of the Consumer Price Index
Shifting the Base
Exercises
Summary
Exercises
Data Analytics
Introduction
Components of a Time Series
Secular Trend
Cyclical Variation
Seasonal Variation
Irregular Variation
A Moving Average
Weighted Moving Average
Exercises
Linear Trend
Least Squares Method
Exercises
Nonlinear Trends
Exercises
Seasonal Variation
Determining a Seasonal Index
Exercises
Deseasonalizing Data
Using Deseasonalized Data to Forecast
Exercises
The Durbin-Watson Statistic
Exercises
Summary
Exercises
Data Analytics
Problems
Practice Test
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
Summary
Pronunciation Key
Exercises
Introduction
Elements of a Decision
Decision Making Under Conditions of Uncertainty
Payoff Table
Expected Payoff
Exercises
Opportunity Loss
Exercises
Expected Opportunity Loss
Exercises
Maximin, Maximax, and Minimax Regret Strategies
Value of Perfect Information
Sensitivity Analysis
Exercises
Decision Trees
Summary
Exercises
Appendix A: Data Sets
Appendix B: Tables
Appendix C: Software Commands
Appendix D: Answers to Odd-Numbered Chapter Exercises
Review Exercises
Solutions to Practice Tests