The proportion of the total variation that is explained by the model: In our example, R-squared = 0.3058. What does this imply? $$R^2 = \frac{SSR}{SST} = \frac{SST - SSE}{SST} = 1 - \frac{SSE}{SST}$$ a. 30.58% of the variation in price can be attributed to sqft b. Almost 70% of all price-variation is explained c. There is 30.58% of the variation in price that cannot be explained by sqft d. This is a good model Moving to another question will save this response.
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R-squared ($R^2$) is a statistical measure that represents the proportion of the variance in the dependent variable that can be explained by the independent variable(s) in a regression model. In simpler terms, it tells you how well the regression model fits the Show more…
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Using technology, it was determined that the total sum of squares (SST) was 3393 and the sum of squares due to error (SSE) was 639.2. Calculate R^2 and determine its meaning. Round your answer to four decimal places. Select the correct answer below: R^2 = 0.8116 Therefore, 81.16% of the variation in the observed y-values can be explained by the estimated regression equation. R^2 = 0.8116 Therefore, 0.8116% of the variation in the observed y-values can be explained by the estimated regression equation. R^2 = 0.1884 Therefore, 0.1884% of the variation in the observed y-values can be explained by the estimated regression equation. R^2 = 0.1884 Therefore, 18.84% of the variation in the observed y-values can be explained by the estimated regression equation.
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If SSR = 63 and SST = 84, compute the coefficient of determination. Type an integer or decimal. Do not round. What is the meaning of r^2? r^2 is the proportion of the variation in the dependent variable that can be explained by the variation in the independent variable. 100% - r^2 is the proportion of the variation in the dependent variable that cannot be explained by the variation in the independent variable. (1 - r^2) * 100% is the proportion of the variation in the independent variable that cannot be explained by the variation in the dependent variable.
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