Bivariate quantitative data refers to a set of data that consists of two quantitative variables. These variables are measured or observed for each individual or case in the dataset.
Think of bivariate quantitative data as a pair of shoes. Each shoe represents one variable, and together they form a complete set. Just like you need both shoes to walk comfortably, bivariate quantitative data requires both variables to provide meaningful insights.
Scatterplot: A scatterplot is a graphical representation of bivariate quantitative data where each point on the plot represents an individual or case with values for both variables.
Correlation Coefficient: The correlation coefficient measures the strength and direction of the linear relationship between two quantitative variables in bivariate data.
Covariance: Covariance is a measure that quantifies how much two variables in bivariate quantitative data vary together. It indicates whether they tend to increase or decrease simultaneously.
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