Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a

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Find The Regression Equation Letting Overhead Width Be The Predictor X Variable Find The Best Predicted Weight Of A 1 (83.11 KiB) Viewed 29 times

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 1,5 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) 8.3 8.2 7.4 166 164 9.9 282 7.8 148 9.7 249 Weight (kg) 198 Critical Values of the Pearson Correlation Coefficient r Click the icon to view the critical values of the Pearson correlation coefficient r. Critical Values of the Pearson Correlation Coefficient r (x=0.01 n x=0.05 NOTE: To test H₂: p= 0.950 against H.: p0, reje The regression equation is y 0.990 0.959 0.917 if the absolute value 0.878 0.811 (Round to one decimal place as needed.) greater than the critica 0.754 0.875 value in the table. The best predicted weight for an overhead width of 1.5 cm is kg. 0.707 0.834 (Round to one decimal place as needed.) 0.866 0.798 0.832 0.765 Can the prediction be correct? What is wrong with predicting the weight in this case? 0.602 0.735 12 0.576 0.708 13 0.553 0.884 14 0.532 0.661 OA. 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. OB. 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. OC. The prediction cannot be correct because a negative weight does not make sense and because there is not sufficient evidence of a linear correlation. OD. The prediction can be correct. There is nothing wrong with predicting the weight in this case. 15 0.514 0.641 16 0.497 0.823 17 0.482 0.606 18 0.468 0.590 19 0.456 0.575 20 0.444 0.561 25 0.396 0.505 30 0.361 0.463 35 0.335 0.430 40 40 0.312 0.402 45 0.294 0.378 50 0.279 0.361 60 0.254 0.330 70 0.236 0.305 80 0.220 0.286 90 0.207 0.269 100 0.196 0.256 n 4 5 6 7 8 9 10 11 " -0.05 = 0.01