IE 6200. Engineering Probability and Statistics (4 Hours)

Course Outline:

1. Fundamental Concepts of Probability

• • Events, sample space, and discrete random variables
  • Density functions, cumulative probability distributions, and moment generating functions

1. Expectation of Random Variables

• • Calculation of expectations in data analytics
  • Common discrete and continuous probability distributions

1. Multivariate Probability Distributions

• • Covariance, independence of random variables
  • Applications in multivariate data analysis

1. Sampling and Descriptive Statistics

• • Overview of sampling techniques
  • Descriptive statistics for data summarization

1. Parameter Estimation, Confidence Intervals, and Hypothesis Testing

• • Statistical inference in data analytics
  • Applications in hypothesis testing and confidence intervals

1. Analysis of Variance

• • Introduction to analysis of variance
  • Applications in comparing multiple groups

Assignments and Assessments:

• Probability and statistics problem sets
• Data analysis projects
• Midterm exam
• Final project: Statistical analysis of a real-world dataset

Lesson Summary

IE 6200 is a course that covers the fundamental concepts of probability and statistics in engineering. It is a 4-hour course that covers various topics such as:

• Events, sample space, and discrete random variables
• Density functions, cumulative probability distributions, and moment generating functions
• Expectation of random variables and calculation of expectations in data analytics
• Common discrete and continuous probability distributions
• Multivariate probability distributions and applications in multivariate data analysis
• Sampling techniques and descriptive statistics for data summarization
• Parameter estimation, confidence intervals, and hypothesis testing in data analytics
• Applications in hypothesis testing and confidence intervals
• Introduction to analysis of variance and applications in comparing multiple groups

The course includes assignments such as problem sets and data analysis projects. It also has a midterm exam and a final project, which involves conducting statistical analysis of a real-world dataset.