The coefficient of determination, is when someone tries to predict the outcome of the testing of a hypothesis, or their guess at to what will happen. It helps determine how well outcomes are determined beforehand.
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The answer depends on the what the leading coefficient is of!
The coefficient is the number that multiplies a value. For example, the coefficient of 4x is 4. If n+3 is in brackets in the form (n+3) then the coefficient is one. If not the the coefficient of n is also 1.
Correlation coefficient My understanding is: two variables as they relate to one another and how accurately you can predict their behavior to one another when together. Basically the strength of the linear association between two variables. When the variables have a tendency to go up and down together, this is a positive correlation coefficient. Variables with a tendency to go up and down in opposition, (one ends up with a high value and the other a low value) this is negatiove correlation coefficient. An example would be the amount of weight a mom gains during pregnancy and the birth weight of the baby
The same as with a positive one.
A correlation coefficient is a value between -1 and 1 that shows how close of a good fit the regression line is. For example a regular line has a correlation coefficient of 1. A regression is a best fit and therefore has a correlation coefficient close to one. the closer to one the more accurate the line is to a non regression line.