Applied Statistics for Scientists and Engineers

Catalog description

This course emphasizes practical decision-making in statistical analysis by comparing parametric and non-parametric methods, with applications to real data problems in engineering and the natural sciences. Topics include sampling techniques, graphical and numerical summaries, inferential statistics (one-sample, two-sample, repeated measures, and multiple comparisons), distribution-free tests (Wilcoxon signed-rank, Mann–Whitney U, Kruskal–Wallis, Kolmogorov Smirnov tests, Friedman, and bootstrap estimation), and an introduction to statistical quality control and reliability analysis. R software is used for computation and data visualization. P: MATH 260 with at least a C grade Fall Odd.