Statistics for Business and Economics, Global Edition

 
 
Pearson HigherEducation (Verlag)
  • 9. Auflage
  • |
  • erschienen am 5. Juli 2019
  • |
  • 800 Seiten
 
E-Book | PDF ohne DRM | Systemvoraussetzungen
978-1-292-31520-1 (ISBN)
 
Newbold:StatsfrB&E GE_p9
9. Auflage
  • Englisch
  • Harlow, United Kingdom
  • |
  • Großbritannien
  • 21,57 MB
978-1-292-31520-1 (9781292315201)
weitere Ausgaben werden ermittelt
  • Cover
  • Title Page
  • Copyright Page
  • 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 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 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
  • CHAPTER17 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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