Applied Statistics

This course is designed to provide engineering students with a robust foundation in statistical analysis, empowering them to extract meaningful insights from data and make informed, data-driven decisions in engineering practice. Recognizing the increasing role of data in engineering innovation, the course emphasizes both the theoretical underpinnings and practical applications of statistical tools. It covers a comprehensive spectrum of topics including descriptive statistics, data visualization, probability theory, hypothesis testing, ANOVA, regression analysis, and non-parametric methods. Building on these fundamentals, the course extends into advanced techniques such as experimental design, multivariate analysis, and time series forecasting, all contextualized within engineering problems and real-world scenarios. The focus is on developing practical skills in data collection, cleaning, modeling, and visualization, along with the ability to communicate and justify statistical findings effectively. By the end of the course, students will be able to summarize and interpret complex datasets, design and evaluate experiments, perform advanced statistical modeling, and synthesize multiple techniques into cohesive analytical approaches. They will demonstrate competence in using statistical software tools, interpreting quantitative results, and applying statistical reasoning to support engineering analysis and decision-making with confidence, clarity, and precision.

• Apply descriptive statistics, data visualization, and probability theory to effectively summarize, interpret, and communicate data.

• Apply hypothesis testing and ANOVA to draw valid statistical inferences, compare group means, and support data-driven engineering decisions.

• Apply regression analysis and non-parametric methods for data modeling, prediction, and problem-solving in engineering applications.

• Design to optimize processes, evaluate factors, and interpret results relevant to engineering challenges.

• Apply advanced statistical methods for complex data interpretation, trend analysis, and informed decision-making in engineering problems.

• Integrate comprehensive statistical knowledge and skills by applying multiple methods and techniques into a cohesive data analysis project, demonstrating proficiency in data collection, cleaning, visualization, and modeling, while effectively interpreting, communicating, and defending statistical results with clear justification.

1. Course Introduction

2. Lecture 1 - Introduction to Applied Statistics

3. Lecture 2 - Looking at Data

4. Lecture 3 - Describing Data

5. Lecture 4 - Producing Data

6. Lecture 5 - Probability Distributions

7. Lecture 6 - Sampling Distributions

8. Lecture 7 - Inferences

9. Lecture 8 - Hypothesis Testing

10. Lecture 9 - Analysis of Variance

11. Lecture 10 - Linear Regression

12. Lecture 11 - Multiple Regression

13. Lecture 12 - Non-parametric Tests

14. Lecture 13 - Design of Experiments

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1. Applied Statistics and Probability for Engineers by Montgomery and Runger (7th Edition)

2. Statistical Techniques in Business & Economics by Lind (15th Edition)

3. Introduction to Statistics and Data Analysis by Peck (3rd Edition)

4. Introductory Statistics by Mann (7th Edition)

5. Statistical Methods by Mohr (4th Edition)

6. Design and Analysis of Experiments by Montgomery (8th Edition)

1. Applied Statistics by Kimberly Brehm

2. Statistics for Applications by MIT OpenCourseWare

3. Applied Statistics by Enlightenment

4. Design of Experiments by Institute of Quality and Reliability

5. Design of Experiments by The Open Educator

6. Statistics by The Organic Chemistry Tutor