Applied Statistics

This course equips engineering students with the statistical tools and techniques necessary to extract meaningful insights from data, enhancing their analytical proficiency and problem-solving capabilities. The course covers a comprehensive range of statistical methods, including descriptive statistics, data visualization, probability theory, hypothesis testing, ANOVA, regression analysis, and non-parametric methods. Students will also explore advanced techniques such as experimental design, multivariate analysis, and time series forecasting, all within the context of engineering applications. Emphasis is placed on practical skills in data collection, cleaning, modeling, and visualization, as well as effective communication and defense of statistical findings. Upon completion of the course, students will be able to apply statistical methods to summarize, interpret, and communicate engineering data effectively. They will develop the skills to design and analyze experiments, perform advanced statistical modeling, and integrate multiple techniques into a cohesive data analysis project. Students will demonstrate proficiency in using statistical software, interpreting results, and making data-driven engineering decisions with confidence and clarity.

• 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

Please visit this link for the updated class schedule.

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