Statistical Techniques for Transportation Engineering is written with a systematic approach in mind and covers a full range of data analysis topics, from the introductory level (basic probability, measures of dispersion, random variable, discrete and continuous distributions) through more generally used techniques (common statistical distributions, hypothesis testing), to advanced analysis and statistical modeling techniques (regression, AnoVa, and time series). The book also provides worked out examples and solved problems for a wide variety of transportation engineering challenges. - Demonstrates how to effectively interpret, summarize, and report transportation data using appropriate statistical descriptors - Teaches how to identify and apply appropriate analysis methods for transportation data - Explains how to evaluate transportation proposals and schemes with statistical rigor
Autorentext
Professor, Head of Civil Engineering and Director of Infrastructure, Osmania University, Hyderabad, Telangana State. He published over 55 research papers in various international and National journals and conferences.
Klappentext
Statistical Techniques for Transportation Engineering is written with a systematic approach in mind and covers a full range of data analysis topics, from the introductory level (basic probability, measures of dispersion, random variable, discrete and continuous distributions) through more generally used techniques (common statistical distributions, hypothesis testing), to advanced analysis and statistical modeling techniques (regression, AnoVa, and time series). The book also provides worked out examples and solved problems for a wide variety of transportation engineering challenges.
- Demonstrates how to effectively interpret, summarize, and report transportation data using appropriate statistical descriptors
- Teaches how to identify and apply appropriate analysis methods for transportation data
- Explains how to evaluate transportation proposals and schemes with statistical rigor
Inhalt
1. An Overview of Statistical Applications 2. Preliminaries 3. Probability 4. Random Variables 5. Curve Fitting 6. Correlation and Regression 7. Sampling 8. Hypothesis Testing 9. Chi-Square Distribution 10. Test of Significance-Small Samples 11. ANOVA (Analysis of Variance) 12. Analysis of Time Series 13. Index Numbers