This course aims to teach students statistical analysis and data interpretation skills. The learning outcomes include understanding central tendency, data dispersion, statistical testing procedures, and various tests such as t-test, F-test, ANOVA, and regression analysis. The course covers tools like Shapiro-Wilk test, Mann-Whitney U test, Chi-squared test, and resampling methods like bootstrapping. The teaching method involves video lectures. This course is intended for students interested in gaining a comprehensive understanding of statistical analysis and its applications in various fields.
Earth - Stats and Data Analysis
Matthew E. Clapham and University of California, Santa Cruz via YouTube
Overview
Syllabus
1: Central tendency (mean and median).
2: Data dispersion.
3: Standard error/confidence intervals.
Statistical testing procedures and the p value.
6: The t test.
7: the F test.
8: ANOVA.
Shapiro-Wilk test.
10: Kolmogorov-Smirnov test.
11: Mann-Whitney U test.
12: Kruskal-Wallis test.
13: Levene's Test.
14: Categorical data (intro and test choice).
15: Exact binomial test/exact multinomial test.
16: Fisher's exact test.
17: Chi-squared test.
18: Pearson product-moment correlation.
19: Non-parametric correlation.
Linear regression.
21: ANCOVA.
22: Logistic regression.
23: Mahalanobis distance.
24: Hotelling T2 test.
25: MANOVA.
Linear mixed effects models.
26: Resampling methods (bootstrapping).
27: Resampling (two-sample tests).
28: Principal Component Analysis.
29: Non-Metric Multidimensional Scaling (NMDS).
30: Maximum likelihood estimation.
Multiple regression.
Partial and semipartial correlation.
Generalized least squares regression.
Factorial ANOVA.
Nested ANOVA.
Time series and first differences.
Statistical power.
Regression with Count Data: Poisson and Negative Binomial.
Taught by
Matthew E. Clapham
