Changes are often more complex than the simple dichotomous changes with dichotomous causal factors

Changes are often more complex than the simple dichotomous changes with dichotomous causal factors

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Changes are often more complex than the simple dichotomous changes with  dichotomous causal factors, just illustrated. The complex case can be easily  illustrated by changing the example to the migration of birds. The first figure below  illustrates a dichotomous change in the number of birds in North and South  America between the dichotomous periods of winter and summer. Each dot could  represent some millions of birds.    Changes can also be continuous like dawn rather than dichotomous like a  room light. Changes can be statistical like the percentage of birds in each  location. Not all need fly south. 100% can be in Canada and 0% in South  America or the reverse or anything in between. In fact, the birds may stop  in the US, Panama, or anywhere in between or some could even migrate 
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Unformatted text preview: backwards. This next figure illustrates most, but not all, elements in the dependent variable (dots or birds) "switching" with a change in the dichotomous independent variable (season). Further, it can be seen that change can be continuous in both its x and y amount. This is illustrated by plotting the data as a function of both all twelve months (A through G) and all ten latitudes (I through X) which gives us a more typical and more useful example. The previous format provided for: Time A through G x x (location of birds) I through X x x Illustrated in the following figure x x Not only do we get continuous x and y, but we can also get weak or strong relationships and positive or negative relationships....
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Changes are often more complex than the simple dichotomous changes with dichotomous causal factors

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