Statistics for STEM

Catalog description

An introduction to descriptive and inferential statistics, emphasizing the combined use of mathematics and programming for Data Science applications. A hands-on experience with data analysis using modern statistical software, including the interpretation of statistical findings. Topics include numerical and graphical summarization of central tendency and spread, probability, normal, binomial, and Poisson distributions. This course also addresses sampling distributions, t-distribution, the chi-squared distribution, estimation, hypothesis testing, Analysis of Variance, linear and multiple regression, and non-parametric tests. Students will work with big data and complete a research project employing simple statistical inference and advanced modeling techniques. 72.00 hours lecture and 18.00 hours laboratory. (Letter grade or Pass/No Pass option)