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Scatter chart
scatter chart
The graph must be a straight line, and it must pass through the origin.
It allows a quick look at the data to establish whether or not there is any relationship between the variables and also an indication of the nature of the relationship: linear, quadratic, power etc.
if it passes through (0,0) then it is a direct variation
it depends on who you are
Scatter chart
They can be looking for the shape of the relationship between the variables (whether it is linear, quadratic, reciprocal), and the domain over which that relationship extends. They may be looking for peaks or troughs in the line, they may be looking for harmonics, or periodicity. The answer really depends on what is being studied.
By definition, if you graph the relationship between two variables and the result is a straight line (of whatever slope) that is a linear relationship. If it is a curve, rather than a straight line, then it is not linear.
Yes. It can give insight as to whether there is a relationship between two variables, and if so, whether the relationship is direct or indirect; whether it is linear, polynomial, exponential, logarithmic; whether or not there are asysmptotic values; whether or not there is clustering; etc.
scatter chart
Words such as "proportional to" "increases as" "decreases as", usually give an indication of a linear relation. If there are words like "Square" "power" "inversely proportional" then most likely not linear.
The graph must be a straight line, and it must pass through the origin.
It allows a quick look at the data to establish whether or not there is any relationship between the variables and also an indication of the nature of the relationship: linear, quadratic, power etc.
if it passes through (0,0) then it is a direct variation
If the points lie on a straight line through the origin, the two variables are in direct proportion.
A scatter plot can be used to see if there is any relationship between two variables. It can also give a general idea of the nature of that relationship (linear, quadratic, logarithmic, inverse square, etc; whether or not the relationship remains constant over the domain, whether or not the variation remains constant (homoscedasticity), and so on.