Statistics for Business and Economics, Global Edition

Cover
Title Page
Copyright
Dedication
About The Authors
Brief Contents
Contents
Preface
Data File Index
Chapter 1. Describing Data: Graphical
	1.1 Decision Making in an Uncertain Environment
		Random and Systematic Sampling
		Sampling and Nonsampling Errors
	1.2 Classification of Variables
		Categorical and Numerical Variables
		Measurement Levels
	1.3 Graphs to Describe Categorical Variables
		Tables and Charts
		Cross Tables
		Pie Charts
		Pareto Diagrams
	1.4 Graphs to Describe Time-Series Data
	1.5 Graphs to Describe Numerical Variables
		Frequency Distributions
		Histograms and Ogives
		Shape of a Distribution
		Stem-and-Leaf Displays
		Scatter Plots
	1.6 Data Presentation Errors
		Misleading Histograms
		Misleading Time-Series Plots
Chapter 2. Describing Data: Numerical
	2.1 Measures of Central Tendency and Location
		Mean, Median, and Mode
		Shape of a Distribution
		Geometric Mean
		Percentiles and Quartiles
	2.2 Measures of Variability
		Range and Interquartile Range
		Box-and-Whisker Plots
		Variance and Standard Deviation
		Coefficient of Variation
		Chebyshev’s Theorem and the Empirical Rule
		z-Score
	2.3 Weighted Mean and Measures of Grouped Data
	2.4 Measures of Relationships Between Variables
		Case Study: Mortgage Portfolio
Chapter 3. Probability
	3.1 Random Experiment, Outcomes, and Events
	3.2 Probability and Its Postulates
		Classical Probability
		Permutations and Combinations
		Relative Frequency
		Subjective Probability
	3.3 Probability Rules
		Conditional Probability
		Statistical Independence
	3.4 Bivariate Probabilities
		Odds
		Overinvolvement Ratios
	3.5 Bayes’ Theorem
		Subjective Probabilities in Management Decision Making
Chapter 4. Discrete Random Variables and Probability Distributions
	4.1 Random Variables
	4.2 Probability Distributions for Discrete Random Variables
	4.3 Properties of Discrete Random Variables
		Expected Value of a Discrete Random Variable
		Variance of a Discrete Random Variable
		Mean and Variance of Linear Functions of a Random Variable
	4.4 Binomial Distribution
		Developing the Binomial Distribution
	4.5 Poisson Distribution
		Poisson Approximation to the Binomial Distribution
		Comparison of the Poisson and Binomial Distributions
	4.6 Hypergeometric Distribution
	4.7 Jointly Distributed Discrete Random Variables
		Conditional Mean and Variance
		Computer Applications
		Linear Functions of Random Variables
		Covariance
		Correlation
		Portfolio Analysis
Chapter 5. Continuous Random Variables and Probability Distributions
	5.1 Continuous Random Variables
		The Uniform Distribution
	5.2 Expectations for Continuous Random Variables
	5.3 The Normal Distribution
		Normal Probability Plots
	5.4 Normal Distribution Approximation for Binomial Distribution
		Proportion Random Variable
	5.5 The Exponential Distribution
	5.6 Jointly Distributed Continuous Random Variables
		Linear Combinations of Random Variables
		Financial Investment Portfolios
		Cautions Concerning Finance Models
Chapter 6. Sampling and Sampling Distributions
	6.1 Sampling from a Population
		Development of a Sampling Distribution
	6.2 Sampling Distributions of Sample Means
		Central Limit Theorem
		Monte Carlo Simulations: Central Limit Theorem
		Acceptance Intervals
	6.3 Sampling Distributions of Sample Proportions
	6.4 Sampling Distributions of Sample Variances
Chapter 7. Estimation: Single Population
	7.1 Properties of Point Estimators
		Unbiased
		Most Efficient
	7.2 Confidence Interval Estimation for the Mean of a Normal Distribution: Population Variance Known
		Intervals Based on the Normal Distribution
		Reducing Margin of Error
	7.3 Confidence Interval Estimation for the Mean of a Normal Distribution: Population Variance Unknown
		Student’s t Distribution
		Intervals Based on the Student’s t Distribution
	7.4 Confidence Interval Estimationfor Population Proportion (Large Samples)
	7.5 Confidence Interval Estimation for the Variance of a Normal Distribution
	7.6 Confidence Interval Estimation: Finite Populations
		Population Mean and Population Total
		Population Proportion
	7.7 Sample-Size Determination: Large Populations
		Mean of a Normally Distributed Population, Known Population Variance
		Population Proportion
	7.8 Sample-Size Determination: Finite Populations
		Sample Sizes for Simple Random Sampling: Estimation of the Population Mean or Total
		Sample Sizes for Simple Random Sampling: Estimation of Population Proportion
Chapter 8. Estimation: Additional Topics
	8.1 Confidence Interval Estimation of the Difference Between Two Normal Population Means: Dependent Samples
	8.2 Confidence Interval Estimation of the Difference Between Two Normal Population Means: Independent Samples
		Two Means, Independent Samples, and Known Population Variances
		Two Means, Independent Samples, and Unknown Population Variances Assumed to Be Equal
		Two Means, Independent Samples, and Unknown Population Variances Not Assumed to Be Equal
	8.3 Confidence Interval Estimation of the Difference Between Two Population Proportions (Large Samples)
Chapter 9. Hypothesis Testing: Single Population
	9.1 Concepts of Hypothesis Testing
	9.2 Tests of the Mean of a Normal Distribution: Population Variance Known
		p-Value
		Two-Sided Alternative Hypothesis
	9.3 Tests of the Mean of a Normal Distribution: Population Variance Unknown
	9.4 Tests of the Population Proportion (Large Samples)
	9.5 Assessing the Power of a Test
		Tests of the Mean of a Normal Distribution: Population Variance Known
		Power of Population Proportion Tests (Large Samples)
	9.6 Tests of the Variance of a Normal Distribution
Chapter 10. Hypothesis Testing: Additional Topics
	10.1 Tests of the Difference Between Two Normal Population Means: Dependent Samples
		Two Means, Matched Pairs
	10.2 Tests of the Difference Between Two Normal Population Means: Independent Samples
		Two Means, Independent Samples, Known Population Variances
		Two Means, Independent Samples, Unknown Population Variances Assumed to Be Equal
		Two Means, Independent Samples, Unknown Population Variances Not Assumed to Be Equal
	10.3 Tests of the Difference Between Two Population Proportions (Large Samples)
	10.4 Tests of the Equality of the Variances Between Two Normally Distributed Populations
	10.5 Some Comments on Hypothesis Testing
Chapter 11. Simple Regression
	11.1 Overview of Linear Models
	11.2 Linear Regression Model
	11.3 Least Squares Coefficient Estimators
		Computer Computation of Regression Coefficients
	11.4 The Explanatory Power of a Linear Regression Equation
		Coefficient of Determination, R2
	11.5 Statistical Inference: Hypothesis Tests and Confidence Intervals
		Hypothesis Test for Population Slope Coefficient Using the F Distribution
	11.6 Prediction
	11.7 Correlation Analysis
		Hypothesis Test for Correlation
	11.8 Beta Measure of Financial Risk
	11.9 Graphical Analysis
Chapter 12. Multiple Regression
	12.1 The Multiple Regression Model
		Model Specification
		Model Objectives
		Model Development
		Three-Dimensional Graphing
	12.2 Estimation of Coefficients
		Least Squares Procedure
	12.3 Explanatory Power of a Multiple Regression Equation
	12.4 Confidence Intervals and Hypothesis Tests for Individual Regression Coefficients
		Confidence Intervals
		Tests of Hypotheses
	12.5 Tests on Regression Coefficients
		Tests on All Coefficients
		Test on a Subset of Regression Coefficients
		Comparison of F and t Tests
	12.6 Prediction
	12.7 Transformations for Nonlinear Regression Models
		Quadratic Transformations
		Logarithmic Transformations
	12.8 Dummy Variables for Regression Models
		Differences in Slope
	12.9 Multiple Regression Analysis Application Procedure
		Model Specification
		Multiple Regression
		Effect of Dropping a Statistically Significant Variable
		Analysis of Residuals
Chapter 13. Additional Topics in Regression Analysis
	13.1 Model-Building Methodology
		Model Specification
		Coefficient Estimation
		Model Verification
		Model Interpretation and Inference
	13.2 Dummy Variables and Experimental Design
		Experimental Design Models
		Public Sector Applications
	13.3 Lagged Values of the Dependent Variable as Regressors
	13.4 Specification Bias
	13.5 Multicollinearity
	13.6 Heteroscedasticity
	13.7 Autocorrelated Errors
		Estimation of Regressions with Autocorrelated Errors
		Autocorrelated Errors in Models with Lagged Dependent Variables
Chapter 14. Analysis of Categorical Data
	14.1 Goodness-of-Fit Tests: Specified Probabilities
	14.2 Goodness-of-Fit Tests: Population Parameters Unknown
		A Test for the Poisson Distribution
		A Test for the Normal Distribution
	14.3 Contingency Tables
	14.4 Nonparametric Tests for Paired or Matched Samples
		Sign Test for Paired or Matched Samples
		Wilcoxon Signed Rank Test for Paired or Matched Samples
		Normal Approximation to the Sign Test
		Normal Approximation to the Wilcoxon Signed Rank Test
		Sign Test for a Single Population Median
	14.5 Nonparametric Tests for Independent Random Samples
		Mann-Whitney U Test
		Wilcoxon Rank Sum Test
	14.6 Spearman Rank Correlation
	14.7 A Nonparametric Test for Randomness
		Runs Test: Small Sample Size
		Runs Test: Large Sample Size
Chapter 15. Analysis of Variance
	15.1 Comparison of Several Population Means
	15.2 One-Way Analysis of Variance
		Multiple Comparisons Between Subgroup Means
		Population Model for One-Way Analysis of Variance
	15.3 The Kruskal-Wallis Test
	15.4 Two-Way Analysis of Variance: One Observation per Cell, Randomized Blocks
	15.5 Two-Way Analysis of Variance: More Than One Observation per Cell
Chapter 16. Time-Series Analysis and Forecasting
	16.1 Components of a Time Series
	16.2 Moving Averages
		Extraction of the Seasonal Component Through Moving Averages
	16.3 Exponential Smoothing
		The Holt-Winters Exponential Smoothing Forecasting Model
		Forecasting Seasonal Time Series
	16.4 Autoregressive Models
	16.5 Autoregressive Integrated Moving Average Models
Chapter 17. Additional Topics in Sampling
	17.1 Stratified Sampling
		Analysis of Results from Stratified Random Sampling
		Allocation of Sample Effort Among Strata
		Determining Sample Sizes for Stratified Random Sampling with Specified Degree of Precision
	17.2 Other Sampling Methods
		Cluster Sampling
		Two-Phase Sampling
		Nonprobabilistic Sampling Methods
Appendix Tables
Index