MATH 341 - Applied Statistical Methods I

5 CR

This class covers probability theory and applications including trees and Venn diagrams, conditional probability, contingency tables, independence, and Bayes theorem. It will cover random variables and sampling distributions (binomial, Poisson, normal, exponential, geometric, and hypergeometric) and their use in confidence intervals and hypothesis testing such as t-tests, z-tests, one and two-sample mean and proportions, chi-squared; ANOVA. The focus will be on statistics in real-world examples from various sources using programming languages R or Python. Students should expect to produce reports and presentations.

Prerequisite(s): BA 240 with a C or better and admission into Data Management and Analysis BAS, Data Analytics Concentration program, or BAS Software Development Artificial Intelligence concentration, or permission of Mathematics Department Chair.

Course Outcomes

Formulate a real world problem into the appropriate statistical model
Calculate probabilities using the appropriate rule, table or diagram
Classify the sampling distributions and calculate probabilities
Choose appropriate calculations for a confidence interval or a hypothesis test
Perform calculations with and without technological tools
Perform appropriate ANOVA model
Interpret results and clearly state conclusions in reports and presentations with close attention to detail

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