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• Cover
• Title
• Copyright
• Dedication
• About the Authors
• Brief Contents
• Contents
• Preface
• Data File Index
• CHAPTER 1 Using Graphs to Describe Data
• 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 Using Numerical Measures to Describe Data
• 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 Elements of Chance: Probability Methods
• 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 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 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 Distributions of Sample Statistics
• 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 Confidence Interval Estimation: One 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 Estimation for 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 Confidence Interval Estimation: Further 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 Tests of a 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 Two Population Hypothesis Tests
• 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 Two Variable Regression Analysis
• 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 Variable Regression Analysis
• 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 Introduction to Nonparametric Statistics
• 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 Forecasting with Time-Series Models
• 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 Sampling: Stratified, Cluster, and Other Sampling Methods
• 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 SpecifiedDegree of Precision
• 17.2 Other Sampling Methods
• Cluster Sampling
• Two-Phase Sampling
• Nonprobabilistic Sampling Methods
• APPENDIX TABLES
• INDEX