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. The regression equation is y=___+___x ​(Round to one decimal place as​ needed.) The best predicted weight for an overhead width of 2.3cm is ___kg.  ​(Round to one decimal place as​ needed.)   Can the prediction be​ correct? What is wrong with predicting the weight in this​ case?

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 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) 7.9 8.4 9.7 7.4 9.6 7.1 Weight (kg) 162 214 263 140 254 155 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y = + x. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2 cm is kg. (Round to one decimal place as needed.) Can the prediction be correct? What is wrong with predicting the weight in this case? 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. O B. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The…

The accompanying data are the length (in centimeters) and girths (in centimeters) of 12 harbor seals. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x= 140 cm E Click the icon to view the table of lengths and girths. (b) x= 172 cm (c) x= 164 cm (d) x= 158 cm The equation of the regression line is y=x+ (Round to two decimal places as needed.) Construct a scatter plot of the data and draw the regression line. Plot length on the horizontal axis and girth on the vertical axis. Choose the correct graph below. O A. OB. Oc. OD. Q 140 140 140 140 100 100 100 100 120 180 120 160 120 180 120 180 (a) Predict the girth for a length of 140 cm, if it is meaningful. Select the correct choice below and, if necessary, fill in the answer box within your…

(2 Find the regression equation, letting the diameter be the predictor (x) variable. Find the best predicted circumference of a marble with a diameter of 1.2 cm. How does the result compare to the actual circumference of 3.8 cm? Use a significance level of 0.05. Baseball Basketball Golf Soccer Tennis Ping-Pong Volleyball Diameter 7.5 24.2 4.4 21.7 6.9 4.0 21.4 Circumference 23.6 76.0 13.8 68.2 21.7 12.6 67.2 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y=+x. (Round to five decimal places as needed.) The best predicted circumference for a diameter of 1.2 cm is cm. (Round to one decimal place as needed.) How does the result compare to the actual circumference of 3.8 cm? O A. Since 1.2 cm is beyond the scope of the sample diameters, the predicted value yields a very different circumference. O B. Even though 1.2 cm is within the scope of the sample diameters, the predicted value yields a very different circumference. O C.…