2 indicate perfect fit (i.e., all data points are on the regression line.)
]]>2 = 0, what shape should we expect the data points in the scatterplot to resemble?
]]>2 is to 100%, the ____________ the data points are to the regression line.
]]>2 tells us the percentage of the _________________ that is explained by the least squares regression line.
]]>2 is a measure of how successfully the regression line explains the *variation* in y.
]]>2 is really close to 100%, then the sum of squared residuals is very small.
]]>2 is really close to 100%, then there is a lot of unexplained variation.
]]>2 measures the fraction of y values that are exactly predicted by the x values.
]]>2 is close to 100%.
]]>2 of 47% and the regression line using variable #2 has an r^{2} of 89%. Which explanatory variable explains the most variation in y?
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