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Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.3 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05 Overhead Width (cm) 7.5 7. 8.7 8.2 9.6 9.9 Weight (kg) 144 170 219 171 252 272 Click the icon to view the critical values of the Pearson correlation coefficient r. Critical Values of the Pearson Correlation Coefficient r - X The regression equation is y = | +|]x. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2.3 cm is | kg- Critical Values of the Pearson Correlation Coefficient r a = 0.05 a = 0.01 (Round to one decimal place as needed.) NOTE: To test Ho: P= 0 0.950 0.990 against H,: p #0. reject Ho Can the prediction be correct? What is wrong with predicting the weight in this case? 0.878 0.95 if the absolute value of r is 0.811 0.917 greater than the critical 10.754 0.87 value in the table. O A. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. 10.707 0.834 O B. The prediction cannot be correct because a negative weight does not make sense and because there is not sufficient evidence of a linear correlation. 0.668 0.798 0.632 0.765 O C. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data. 0.602 0.735 O D. The prediction can be correct. There is nothing wrong with predicting the weight in this case. 0.576 10.708 13 0.553 0.684 0.532 10.66 15 10.514 0.641 16 0.497 0.623 0.482 0.606 0.468 10.580 10.456 10.575 0.444 10.561 0.396 10.505 0.361 0.463 0.335 10.430 0.312 0.402 0.294 0.378 0.279 10.361 0.254 0.330 0.236 0.305 0.220 0.286 0.207 0.269 D. 196 0.256 X = 0.05 a = 0.01