A scientific study on calorie intake gives the following data table. Calorie intake (1000) Weight gained (Ounces) 70 28 72 34 76 23 80 24 84 13 Using technology, it was determined that the total sum of squares (SST) was 237.2, the sum of squares regression (SSR) was 177.96, and the sum of squares due to error (SSE) was 59.244. Calculate R2 and determine its meaning. Round your answer to four decimal places. Select the correct answer below: R2=0.2498 Therefore, 24.98% of the variation in the observed y-values can be explained by the estimated regression equation. R2=0.7503 Therefore, 75.03% of the variation in the observed y-values can be explained by the estimated regression equation. R2=1.3329 Therefore, 13.329% of the variation in the observed y-values can be explained by the estimated regression equation. R2=0.3329 Therefore, 33.29% of the variation in the observed y-values can be explained by the estimated regression equation.
A scientific study on calorie intake gives the following data table. Calorie intake (1000) Weight gained (Ounces) 70 28 72 34 76 23 80 24 84 13
Using technology, it was determined that the total sum of squares (SST) was 237.2, the sum of squares regression (SSR) was 177.96, and the sum of squares due to error (SSE) was 59.244. Calculate R2 and determine its meaning. Round your answer to four decimal places.
Select the correct answer below:
R2=0.2498 Therefore, 24.98% of the variation in the observed y-values can be explained by the estimated regression equation.
R2=0.7503 Therefore, 75.03% of the variation in the observed y-values can be explained by the estimated regression equation.
R2=1.3329 Therefore, 13.329% of the variation in the observed y-values can be explained by the estimated regression equation.
R2=0.3329 Therefore, 33.29% of the variation in the observed y-values can be explained by the estimated regression equation.
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