| 1. To introduce the fundamental concepts of statistics. 2. To understand the basic concepts and theoretical framework of engineering statistics. 3. To become familiar with commonly used statistical methods and correctly apply them to engineering data analysis. 4. To develop the ability to analyze data, interpret results, and support engineering decision-making. |
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| topic Discussion/Production |
| Discussion |
| Lecturing |
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| Oral Presentation |
| Assignment |
| Quiz |
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| Week |
Course Content |
| Week 1 |
Introduction: Course overview, applications of statistics and geostatistics in engineering geology, and examples of geological and geotechnical data. |
| Week 2 |
Basic Concepts of Statistics: Data types, descriptive statistics, probability distributions, histograms, boxplots, and outlier identification. |
| Week 3 |
Spatial Random Variables and Regionalized Variables: Spatial data, random fields, regionalized variables, spatial dependence, and stationarity. |
| Week 4 |
Simple and Multiple Linear Regression: Correlation, simple linear regression, multiple linear regression, coefficient interpretation, and residual analysis. |
| Week 5 |
Variogram Models and Parameter Estimation: Semivariance, experimental variograms, nugget, sill, range, common variogram models, and parameter estimation. |
| Week 6 |
Kriging Principles and Interpolation Framework: Spatial interpolation, kriging weights, ordinary kriging, unbiased estimation, and prediction using spatial data. |
| Week 7 |
Estimation Error and Uncertainty Analysis: Kriging variance, prediction uncertainty, effects of sampling density, and interpretation of estimation errors. |
| Week 8 |
Midterm Exam |
| Week 9 |
Comparing Interpolation Methods: Kriging, inverse distance weighting, other interpolation methods, cross-validation, and comparison of prediction performance. |
| Week 10 |
Multivariate Analysis: Covariance and correlation matrices, relationships among multiple variables, principal component analysis, and interpretation of multivariate data. |
| Week 11 |
Self-Directed Learning: Independent analysis of geological or geotechnical data, data visualization, statistical exploration, and identification of spatial patterns. |
| Week 12 |
Geological and Engineering Applications: Applications of geostatistics to site investigation, subsurface characterization, geotechnical properties, and engineering decision-making. |
| Week 13 |
Bayesian Methods: Prior, likelihood, posterior distributions, parameter uncertainty, and basic Bayesian applications in geological and engineering data analysis. |
| Week 14 |
Final Project Presentations I |
| Week 15 |
Final Project Presentations II |
| Week 16 |
Self-Directed Learning and Course Integration: Revision of final projects, integration of statistical and geostatistical methods, and final project submission. |
self-directed
learning |
02.Viewing multimedia materials related to industry and academia.
03.Preparing presentations or reports related to industry and academia.
Self-directed learning includes data collection, reading course materials, and preparation of group reports, which take place throughout the semester and are completed at the end. |
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”Engineering Statistics (5th Ed),” by Montgomery et al., Wiley, 2012.
Navidi, W., Statistics for Engineers and Scientists, 6th Edition, McGraw-Hill, 2022
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