It looks like you're mentioning different types of associations, often used in statistics and data analysis to describe relationships between variables. Here's a brief explanation of each term:
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Linear Association: This occurs when there is a straight-line relationship between two variables. If you plot the data on a scatter plot, you would see points that tend to cluster around a straight line. This can be positive (both variables increase together) or negative (one variable increases while the other decreases).
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Association: In general terms, association refers to any relationship or correlation between two variables. This can be linear, nonlinear, positive, or negative.
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Negative Association: This specifies a type of association where, as one variable increases, the other variable tends to decrease. In a scatter plot, this would typically show a downward slope.
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Non-Linear: This refers to a relationship between variables that does not form a straight line when plotted on a graph. Instead, the relationship may take on various shapes, such as curves or more complex patterns.
If you have a specific context or data set in mind, please provide more details, and I can help analyze or elaborate further!