Statistical Techniques for Transportation Engineering
- 1st Edition - March 13, 2017
- Imprint: Butterworth-Heinemann
- Authors: Kumar Molugaram, G Shanker Rao
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 1 5 5 5 - 8
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 1 1 6 4 2 - 5
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 (ba… Read more
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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
Civil Engineers, Transportation Engineers
Chapter 1. An Overview of Statistical Applications
- Abstract
- 1.1 Introduction
- 1.2 Probability Functions and Statistics
- 1.3 Applications of Normal Distribution
- 1.4 Confidence Bounds
- 1.5 Determination of Sample Size
- 1.6 Random Variables Summation
- 1.7 The Binomial Distributions
- 1.8 The Poisson Distribution
- 1.9 Testing of Hypothesis
- 1.10 Summary
Chapter 2. Preliminaries
- Abstract
- 2.1 Introduction
- 2.2 Basic Concepts
- 2.3 Tabulation of Data
- 2.4 Frequency Distribution
- 2.5 Cumulative Frequency Table
- 2.6 Measures of Central Tendency
- 2.7 Arithmetic Mean
- 2.8 Median
- 2.9 Mode
- 2.10 Geometric Mean
- 2.11 Harmonic Mean
- 2.12 Partition Values (Quartiles, Deciles, and Percentiles)
- 2.13 Measures of Dispersion
- 2.14 Range
- 2.15 Interquartile Range
- 2.16 Quartile Deviation
- 2.17 Mean Deviation
- 2.18 Standard Deviation
Chapter 3. Probability
- Abstract
- 3.1 Introduction
- 3.2 Classical Probability
- 3.3 Relative Frequency Approach of Probability
- 3.4 Symbolic Notation
- 3.5 Axiomatic Theory of Probability
- 3.6 Independent and Dependent Events
- 3.7 Conditional Probability
- 3.8 Multiplication Theorem on Probability
- 3.9 Baye’s Theorem
Chapter 4. Random Variables
- Abstract
- 4.1 Introduction
- 4.2 Discrete Random Variable
- 4.3 Probability Distribution for a Discrete Random Variable
- 4.4 Mean and Variance of a Discrete Distribution
- 4.5 Continuous Random Variable
- 4.6 Probability Density Function
- 4.7 Cumulative Distribution Function
- 4.8 Mean and Variance of a Continuous Random Variable
- 4.9 Joint Distributions
- 4.10 Conditional Probability Distribution
- 4.11 Independent Random Variables
- 4.12 Joint Probability Function of Continuous Random Variables
- 4.13 Joint Probability Distribution Function of Continuous Random Variables
- 4.14 Marginal Distribution Function
- 4.15 Conditional Probability Density Functions
- 4.16 Mathematical Expectation and Moments
- 4.17 Moments
- 4.18 Moment Generating Function
- 4.19 Properties of Moment Generating Function
- 4.20 Discrete Probability Distributions
- 4.21 Poisson Distribution
- 4.22 Discrete Uniform Distribution
- 4.23 The Negative Binomial and Geometric Distribution
- 4.24 Geometric Distribution
- 4.25 Continuous Probability Distributions
- 4.26 Normal Distribution
- 4.27 Characteristic Function
- 4.28 Gamma Distribution
- 4.29 Beta Distribution of First Kind
- 4.30 Weibull Distribution
Chapter 5. Curve Fitting
- Abstract
- 5.1 Introduction
- 5.2 The Method of Least Squares
- 5.3 The Least-Squares Line
- 5.4 Fitting a Parabola by the Method of Least Squares
- 5.5 Fitting the exponential curve of the form y=a ebx
Chapter 6. Correlation and Regression
- Abstract
- 6.1 Introduction
- 6.2 Correlation
- 6.3 Coefficient of Correlation
- 6.4 Methods of Finding Coefficient of Correlation
- 6.5 Scatter Diagram
- 6.6 Direct Method
- 6.7 Spearman’s Rank Correlation Coefficient
- 6.8 Calculation of r (Correlation Coefficient) (Karl Pearson’s Formula)
- 6.9 Regression
- 6.10 Regression Equation
- 6.11 Curve of Regression
- 6.12 Types of Regression
- 6.13 Regression Equations (Linear Fit)
- 6.14 Angle between Two Lines of Regression
- 6.15 Coefficient of Determination
- 6.16 Coefficient Nondetermination
- 6.17 Coefficient of Alienation
- 6.18 Multilinear Regression
- 6.19 Uses of Regression Analysis
Chapter 7. Sampling
- Abstract
- 7.1 Introduction
- 7.2 Population
- 7.3 Sample
- 7.4 Sampling
- 7.5 Random Sampling
- 7.6 Simple Random Sampling
- 7.7 Stratified Sampling
- 7.8 Systematic Sampling
- 7.9 Sample Size Determination
- 7.10 Sampling Distribution
Chapter 8. Hypothesis Testing
- Abstract
- 8.1 Introduction
- 8.2 Hypothesis
- 8.3 Hypothesis Testing
- 8.4 Types of Hypothesis
- 8.5 Computation of Test Statistic
- 8.6 Level of Significance
- 8.7 Critical Region
- 8.8 One-Tailed Test and Two-Tailed Test
- 8.9 Errors
- 8.10 Procedure for Hypothesis Testing
- 8.11 Important Tests of Hypothesis
- 8.12 Critical Values
- 8.13 Test of Significance—Large Samples
- 8.14 Test of Significance for Single Proportion
- 8.15 Testing of Significance for Difference of Proportions
Chapter 9. Chi-Square Distribution
- Abstract
- 9.1 Introduction
- 9.2 Contingency Table
- 9.3 Calculation of Expected Frequencies
- 9.4 Chi-Square Distribution
- 9.5 Mean and Variance of Chi-Square
- 9.6 Additive Property of Independent Chi-Square Variate
- 9.7 Degrees of Freedom
- 9.8 Conditions for Using Chi-Square Test
- 9.9 Uses of Chi-Square Test
Chapter 10. Test of Significance—Small Samples
- Abstract
- 10.1 Introduction
- 10.2 Moments About Mean
- 10.3 Properties of Probability Curve
- 10.4 Assumptions for t-Test
- 10.5 Uses of t-Distribution
- 10.6 Interval Estimate of Population Mean
- 10.7 Types of t-Test
- 10.8 Significant Values of t
- 10.9 Test of Significance of a Single Mean
- 10.10 Student’s t-Test for Difference of Means
- 10.11 Paired t-Test
- 10.12 F-Distribution
Chapter 11. ANOVA (Analysis of Variance)
- Abstract
- 11.1 Introduction
- 11.2 Assumptions
- 11.3 One-Way ANOVA
- 11.4 Working Rule
Chapter 12. Analysis of Time Series
- Abstract
- 12.1 Introduction
- 12.2 Purpose of Time Series Study
- 12.3 Editing of Data
- 12.4 Components of Time Series
- 12.5 Mathematical Model for a Time Series
- 12.6 Methods of Measuring Trend
Chapter 13. Index Numbers
- Abstract
- 13.1 Introduction
- 13.2 Definitions and Characteristics
- 13.3 Types of Index Numbers
- 13.4 Problems in the Construction of Index Numbers
- 13.5 Method of Constructing Index Numbers
- 13.6 Tests for Consistency of Index Numbers
- 13.7 Quantity Index Numbers
- 13.8 Consumer Price Index Number
- 13.9 Chain Base Method
- 13.10 Base Conversion
- 13.11 Splicing
- 13.12 Deflation
- Edition: 1
- Published: March 13, 2017
- Imprint: Butterworth-Heinemann
- Language: English
KM
Kumar Molugaram
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.
Affiliations and expertise
Professor, Head of Civil Engineering and Director of Infrastructure, Osmania University, Hyderabad, Telangana State, IndiaGR
G Shanker Rao
G. Shanker Rao has over 35 years of teaching experience. He has been teaching Numerical Analysis, Operations Research, Statisics and Graph Theory for the last 25 years. Presently he is a member of staff in the Department of Mathematics at University College of Engineering (A), Osmania University.
Affiliations and expertise
University College of Engineering, Osmania University, Hyderabad, IndiaRead Statistical Techniques for Transportation Engineering on ScienceDirect