This document discusses various statistical analyses that can be used to analyze the relationship between variables: 1. Bivariate correlation measures the strength and direction of association between two variables using Pearson's correlation coefficient (r). A significant correlation is when p<0.05. 2. Linear regression analysis uses linear equations to model relationships between a dependent variable and one or more independent variables. It identifies outliers that do not follow patterns. 3. Multiple regression extends linear regression to multiple independent variables, allowing analysis of their collective influence on a dependent variable. It provides measures like R and R-squared of the model's accuracy and fit.