This is a video presented by Alissa Grant-Walker on how to calculate the coefficient of determination. For more information, please see [ Video Examples Example 1 To account for this, an adjusted version of the coefficient of determination is sometimes used. Thus, in the example above, if we added another variable measuring mean height of lecturers, $R^2$ would be no lower and may well, by chance, be greater - even though this is unlikely to be an improvement in the model. This means that the number of lectures per day account for $89.5$% of the variation in the hours people spend at university per day.Īn odd property of $R^2$ is that it is increasing with the number of variables.
There are a number of variants (see comment below) the one presented here is widely used It is therefore important when a statistical model is used either to predict future outcomes or in the testing of hypotheses. In the context of regression it is a statistical measure of how well the regression line approximates the actual data. So, once again, this probability is saying- we can't say equally likely events.
So it's 100 minus 60, or 40, or as a decimal, 0.4, or as a fraction, 4/10, or as a simplified fraction, 2/5. The coefficient of determination, or $R^2$, is a measure that provides information about the goodness of fit of a model. The probability of tails is going to be 100 minus the probability of getting heads, and this, of course, is 60.
Contents Toggle Main Menu 1 Definition 2 Interpretation of the $R^2$ value 3 Worked Example 4 Video Examples 5 External Resources 6 See Also Definition
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