MATH 270 Probability and Statistical Models • 5 Cr.
The fundamentals of probability-based statistics with a focus on data-based problem solving. Introduces probability axioms and principles of randomness to model and evaluate samples from discrete, continuous, univariate, and multivariate distributions. Varying statistical techniques (with use of software such as MATLAB or R) will be included. Prerequisite: MATH& 152 with a B- or better. Recommended: MATH& 153.
After completing this class, students should be able to:
- Model real-world problems by an appropriate probability distribution.
- - Calculate probabilities using appropriate distributions, theorems, diagrams, or software tools.
- - Formulate, fit, and apply appropriate statistical models. Assess and improve the fit of these models.
- - Choose appropriate calculations for a confidence interval/hypothesis test: do so based on theory and simulation (including bootstrapping).
- - Use technological tools such as MATLAB or R to manage and analyze data sets in various sizes and formats.
- - Interpret statistical results and clearly state the conclusion in reports and presentations with close attention to details.
- Spring 2019 (current quarter)