Statistics for Business and Economics, Global Edition

Höfundar: Paul Newbold; William L. Carlson; Betty Thorne (Útgáfa: 10)
Statistics for Business and Economics, Global Edition

Kaup valmöguleikar

Klassísk kennslubók sem er rómuð fyrir nákvæmni og tölfræðilega vandvirkni. Statistics for Business and Economics gerir lesendum kleift að greina hagnýt viðfangsefni af alvöru í stað þess að keyra einfaldar staðlaðar lausnir. Bókin er stærðfræðilega kröfuharðari en flestar kennslubækur í viðskiptatölfræði og veitir þá þekkingu sem þarf til að verða öflugri greinandi og standa undir stjórnunarstörfum síðar.

Nánar um bókina

Útgefandi
Pearson International Content
ISBN
9781292436906
Print ISBN
9781292436845
Format
Page Fidelity (PDF)
Útgáfa
10
Höfundar
Paul Newbold; William L. Carlson; Betty Thorne
Tungumál
English
Útgefið
2022-05-24
Prent takmörkun á líftíma
100
Prent takmörkun
2
Afritunar takmörkun
2
Flokkar
Hagfræði

Kaflar

  • 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