Correlation Analysis in R: Pearson vs Spearman Correlations in R

Correlation Analysis measures the strength and direction of a relationship between two continuous or ordinal variables. In R Studio, choosing between Pearson and Spearman correlation depends on data linearity, normality, and the presence of outliers. Join Dr. Shahbaz S (PhD & Post Doc) as he demonstrates how to test key assumptions, calculate correlation coefficients, interpret significance tests, and produce high-quality scatter plots. What You Will Learn Core Concepts: Linear vs monotonic relationships, correlation strength (r vs rho), and assumption testing. R Implementation: Running Pearson correlation for parametric data and Spearman correlation for non-parametric data. Result Interpretation: Evaluating correlation coefficients, p-values, directionality, and statistical significance. Visualization: Creating scatter plots with trend lines and correlation matrices in R Studio. APA Reporting: Writing concise, publication-ready correlation results for academic journals. Tags #RStats #CorrelationAnalysis #PearsonCorrelation #SpearmanCorrelation #DataAnalysis #RStudio #Statistics #DataScience #Biostatistics #HypothesisTesting