This course covers the properties of correlation, geometrical interpretation of Pearson's correlation coefficient, partial correlation, multiple linear regression, Spearman's rank correlation, Kendall's Tau rank correlation, and power & sample size in R. The course teaches how to calculate correlations, interpret their results, and understand their significance in statistical analysis. The intended audience for this course includes students, researchers, and professionals looking to deepen their understanding of correlation analysis in statistics.
Overview
Syllabus
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.
Taught by
statisticsmatt
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Reviews
5.0 rating, based on 1 Class Central review
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nice it was good topics were delved in to great details and taught very carefully and precisely and to a keen observer a like me it was a great pleasure to watch.math always makes me happy and seeing it makes me more happier
