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Statistics for Business and Economics, Global Edition

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Efnisyfirlit

  • 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 Unknow
      • 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
    • 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
  • 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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Vörumerki: Pearson
Vörunúmer: 9781292315201
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Statistics for Business and Economics, Global Edition

Vörumerki: Pearson
Vörunúmer: 9781292315201
Rafræn bók. Uppl. sendar á netfangið þitt eftir kaup

Veldu vöru

5.790 kr.
Get the product now
5.790 kr.