Correlation

Explore the fundamental concepts and advanced applications of correlation in statistics through this comprehensive 2.5-hour tutorial. Delve into the properties and geometrical interpretation of Pearson's correlation coefficient, and master the intricacies of partial correlation across multiple scenarios. Examine multiple correlation, semipartial correlation, and various rank correlation methods including Spearman's and Kendall's Tau. Investigate the correlation of bivariate discrete distributions and the relationship between variate values and corresponding ranks. Learn to perform power and sample size calculations for multiple correlation coefficients using R, and understand the eigenvalue inequality for correlation matrices. Gain practical insights and theoretical knowledge to enhance your statistical analysis skills.

Properties of Correlation.
Geometrical Interpretation of Pearson's Correlation Coefficient.
Partial Correlation (part 1/4): General Concept.
Partial Correlation (part 2/4): Multiple Linear Regression.
Partial Correlation (part 3/4): Via Zero Order Correlations.
Partial Correlation (part 4/4): Recursive Formula.
Multiple Correlation.
Semipartial (Part) Correlation (part 1/2): General Concept.
Semipartial (Part) Correlation (part 2/2): Type I S.S..
Spearman's Rank Correlation.
Kendall's Tau Rank Correlation.
Correlation of a Bivariate Discrete Distribution.
Correlation between variate values and corresponding ranks (part 1).
Correlation between variate values and corresponding ranks (part 2).
Correlation between variate values and corresponding ranks (part 3): Illustration Using R..
Power & Sample Size in R: Multiple Correlation Coefficient.
Eigenvalue Inequality for a Correlation Matrix.