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grad_stats

Graduate statistics course covering basic estimation, the general linear model, and applications into more complex crossed and nested models.

All analyses are taught using the general linear model and a model comparison approach.

Please note that all of the materials in this repository are based on supporting materials for a graduate statistics classes taught by Keith Lohse while at the University of Utah and Auburn University. The supplemental text for the course was: Judd, C. M., McClelland, G. H., & Ryan, C. S. (2011). Data analysis: A model comparison approach. Routledge.

Video lectures are all available on YouTube (linked below). Note that Sections 1-11 focus on conceptual understanding of the different methods and study designs (corresponding slides are in the "lectures" folder). Section 12 is an applied "how to" section about how to implement these analyses in R using example datasets (all scripts are in the "scripts" folder), which assumes a deeper unstanding of the concepts. I strongly recommend you do not jump into the application section without a solid understanding of the fundamentals covered in the previous sections. Example lab assignments to test your understanding (with answer keys) are in the "lab_assignments" folder.

Section 1: Introduction to Statistics

Section 2: Descriptive Statistics

Section 3: Sampling and the Central Limit Theorem

Section 4: Fundamentals of Hypothesis Tests - aka One Sample T-Tests

Section 5: Finding the Line of Best Fit - aka Pearson's Correlation and Simple Regression

Section 6: Regression with Categorical Predictor - aka Independent Samples T-Test

Section 7: Models with Multiple Categorical Predictors - aka One-Way ANOVA

Section 8: Multiple Categorical Factors with Multiple Levels - aka Multi-Way ANOVA

Section 9: Mixing Continuous and Categorical Factors - aka AN(C)OVA

Section 10: Dealing with Statistically Dependent Measures - aka Paired Sample T-Test and RM ANOVA

Section 11: Other Topics: Statiscal Power, Regression Diagnostics, Etc.

Section 12: How-To Videos Using R

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