Objectives
Master concepts of statistical data analysis, probability theory and statistical inference to understanding and applying such concepts to solve real-life problems in engineering and science.
Program
- Probability: Probability axioms. Conditional probability and independence of events. Total probability rule. Bayes Theorem.
- Random Variables and Multiples Random Variables: Discrete and continuous random variables. Cumulative distribution function. Marginal and conditional distributions. Moments.
- Discrete and Continuous Probability Distributions.
- Sample Moments and Their Distribution. Point Estimation.
- Confidence Intervals on Parameters of One or Two Normal Distributions.
- Tests of Hypotheses on the Parameters of One or Two Normal Distributions. The Chi-square Test for goodness of fit.
- Simple Linear Regression: Slope and intercept estimation. Inference on the model's parameters. Adequacy of the regression model.
Teaching Methodologies
The theory classes will have an expository component followed by some application examples. The teacher/student interaction will also be an important component in the learning process in both theory and practice classes.
Bibliography
See above.
