QUANTITATIVE

• Correlation is a statistical technique that can show whether and how strongly pairs of variables are related.
• The relationship between height and weight isn't perfect; people of the same height vary in weight, and you can easily think of two people you know where the shorter one is heavier than the taller one.
• Correlation can tell you just how much of the variation in peoples' weights is related to their heights.
• Unsuspected correlations can exist in your data.
• Correlation analysis can help understand your data and lead to a greater understanding of your business.
• Correlation is appropriate for certain kinds of data, such as quantifiable data in which numbers are meaningful, usually quantities of some sort.
• Correlation cannot be used for purely categorical data, such as gender, brands purchased, or favorite color.
• Correlation analysis is the study of the relationship between independent and dependent variables.
• Correlation measures the strength and direction of continuous bivariate data.
• A scatter plot is a visual representation of the linear relationship between the two variables.
• A scatter plot involves the x- and y-axes.
• A correlation of r = 0.9 suggests a strong, positive association between two variables, whereas a correlation of r = -0.2 suggest a weak, negative association.
• A correlation close to zero suggests no linear association between two continuous variables.
• X is the independent variable.
• Y is the dependent variable.