A straight line of points going from top left towards bottom right.
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"In curvilinear relationships, the data points increase together up to a certain point (like a positive relationship) and then as one increases, the other decreases (negative relationship) or vice versa." A linear relation is very simple: if one variable goes up, the other goes up (positive correlation) or goes down (negative correlation). A curvilinear relation between variables is non-linear (i.e., that cannot be described by a straight line). Basically, anythig not linear is curvilinear.
Scatter-plot shows correlation between two different variables (one on the y-axis, the other on x-axis). If there is linear correlation, the scatter-points form a straight line from zero (origo) to some direction. The more cloud-like distribution the scatter-plot does have, the less those variables in question have correlation or dependence with each other.
An equation is the same as a function. Identifying a functional relationship from a graph is nearly impossible unless it is trivially simple like a linear relationship.
Most functions are not like linear equations.
A way to look at how one set of data is related to another is called correlation analysis. This statistical method assesses the strength and direction of the relationship between two variables, indicating whether they move together (positive correlation), move in opposite directions (negative correlation), or have no discernible relationship. Tools such as scatter plots and correlation coefficients, like Pearson's r, are commonly used to visualize and quantify these relationships.