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In this course, you will learn:
- This course offers a computer-based introduction to probability and data analytics. It is intended to provide students with the information and practical experience necessary to analyze lab and field data.
- Basic probability concepts are given first because they provide a systematic approach to describing uncertainty. They provide the foundation for the study of quantitative data in science and engineering.
- The MATLAB® programming language is used to conduct virtual experiments and analyze real-world data sets, which are frequently acquired via the internet. Displaying and evaluating data sets, investigating hypotheses, and identifying potential casual correlations between variables are all examples of programming applications.
- This semester marks the first time that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), will be offered and taught together.
Syllabus:
- Programming in MATLAB®
- Descriptive Statistics
- Probability
- MATLAB® Operations
- Joint Probability, Independence, Repeated Trials
- Combinatorial Methods
- MATLAB® Tests and Loops
- Random Variables and Probability Distributions
- Virtual Experiments
- Expectation, Functions of a Random Variable
- Risk
- Some Common Probability Distributions
- Multivariate Probability
- Functions of Many Random Variables
- Time Series and Central Limit Theorem
- Populations and Samples
- Estimation
- Confidence Intervals
- Review
- Testing Hypotheses about a Single Population
- Testing Hypotheses about Two Populations
- Small Samples
- Analysis of Variance (ANOVA)
- Multifactor Analysis of Variance
- Linear Regression
- Analyzing Regression Results
