Statistics for Business and Economics, Ebook, Global Edition

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This title is a Pearson Global Edition. The Editorial team at Pearson has worked closely with educators around the world to include content which is especially relevant to students outside the United States. For courses in Business Statistics. A classic text for accuracy and statistical precision Statistics for Business and Economics enables students to conduct serious analysis of applied problems rather than running simple "canned" applications. This text is also at a mathematically higher level than most business statistics texts and provides students with the knowledge they need to beco. 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