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 You will see that the both the graph and also the equation change.
It is therefore important when a statistical model is used either to predict future outcomes or in the testing of hypotheses. To do so, in your desired graph, draw the corresponding trend-line and select the trend-line equation to be shown Then using the option 'Switch Row/Column' in design tab, switch the graph. In the context of regression it is a statistical measure of how well the regression line approximates the actual data. The coefficient of determination, or $R^2$, is a measure that provides information about the goodness of fit of a model. 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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