Efnisyfirlit

• 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 Unknow
    • 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
  • 8.2 Confidence Interval Estimation of the Difference Between Two Normal Population Means: Independen
    • 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 Sampl
• 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

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