Describing tools commonly used in the field, this textbook provides an understanding of a broad range of analytical tools required to solve transportation problems. It includes a wide breadth of examples and case studies in various aspects of transportation planning, engineering, safety, and economics.
Describing tools commonly used in the field, this textbook provides an understanding of a broad range of analytical tools required to solve transportation problems. It includes a wide breadth of examples and case studies in various aspects of transportation planning, engineering, safety, and economics.
Simon Washington, Matthew G. Karlaftis, Fred Mannering, Panagiotis Anastasopoulos
Inhaltsangabe
Section I Fundamentals 1. Statistical Inference I: Descriptive Statistics 2. Statistical Inference II: Interval Estimation, Hypothesis Testing, and Population Comparisons Descriptive Statistics Section II Continuous Dependent Variable Models 3. Linear Regression 4. Violations of Regression Assumptions Simultaneous Equation Models 6. Panel Data Analysis 7. Background and Exploration in Time Series 8. Forecasting in Time Series: Autoregressive Integrated Moving Average (ARIMA) Models and Extensions 9. Latent Variable Models 10. Duration Models Section III Count and Discrete-Dependent Variable Models11. Count Data Models 12. Logistic Regression 13. Discrete Outcome Models 14. Ordered Probability Models 15. Discrete/Continuous Models Section IV Other Statistical Methods 16. Random Parameters Models 17. Latent Class (Finite Mixture) Models 18. Bivariate and Multivariate Dependent Variable Models 19. Bayesian Statistical Methods
Section I Fundamentals 1. Statistical Inference I: Descriptive Statistics 2. Statistical Inference II: Interval Estimation, Hypothesis Testing, and Population Comparisons Descriptive Statistics Section II Continuous Dependent Variable Models 3. Linear Regression 4. Violations of Regression Assumptions Simultaneous Equation Models 6. Panel Data Analysis 7. Background and Exploration in Time Series 8. Forecasting in Time Series: Autoregressive Integrated Moving Average (ARIMA) Models and Extensions 9. Latent Variable Models 10. Duration Models Section III Count and Discrete-Dependent Variable Models11. Count Data Models 12. Logistic Regression 13. Discrete Outcome Models 14. Ordered Probability Models 15. Discrete/Continuous Models Section IV Other Statistical Methods 16. Random Parameters Models 17. Latent Class (Finite Mixture) Models 18. Bivariate and Multivariate Dependent Variable Models 19. Bayesian Statistical Methods
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