Find the regression equation, letting the diameter be the predictor (x) variable. Find the best predicted circumference of a marble with a diameter of 1.8 cm. How does the result compare to the actual circumference of 5.7 cm? Use a significance level of 0.05. Baseball Basketball Golf Soccer Tennis Ping-Pong Volleyball O Diameter 7.5 23.9 4.2 21.6 6.9 4.1 20.9 Circumference 23.6 75.1 13.2 67.9 21.7 12.9 65.7 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.)

Find the regression equation, letting the diameter be the predictor (x) variable. Find the best predicted circumference of a marble with a diameter of 1.8 cm. How does the result compare to the actual circumference of 5.7 cm? Use a significance level of 0.05. Baseball Basketball Golf Soccer Tennis Ping-Pong Volleyball O Diameter 7.5 23.9 4.2 21.6 6.9 4.1 20.9 Circumference 23.6 75.1 13.2 67.9 21.7 12.9 65.7 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.)

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 6PPS

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Find the regression equation, letting the diameter be the predictor (x) variable. Find the best predicted circumference of a beachball with a diameter of 44.4 cm. How does the result compare to the actual circumference of 139.5 cm? Use a significance level of 0.05. Baseball Basketball Golf Soccer Tennis Ping-Pong Volleyball Diameter 7.3 23.8 4.2 22.3 7.1 4.0 21.2 Circumference 22.9 74.8 13.2 70.1 22.3 12.6 66.6 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r.
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) 7.2 8.3 8.8 8.1 9.9 9.8 Weight (kg) 121 195 210 156 250 253
Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.8cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) 7.1 7.3 9.9 9.3 8.8 8.3 Weight (kg) 137 176 282 230 230 214 The regression equation is y=+x. (Round the constant to the nearest integers needed. Round the coefficient to one decimal place as needed.) The best-predicted weight for an overhead width of 1.8 cm, based on the regression equation, is: ____ kg. (Round to one decimal place as needed.) Can the prediction be correct? If not, what is wrong? A. The prediction cannot be correct because a weight of zero does not…
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 2.1cm. 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. overhead width (cm) 8.2 7.4 9.6 7.9 7.6 8.7 weight (kg) 159 156 241 144 154 207 n α=0.05 α = 0.01 4 0.950 0.990 5 0.878 0.959 6 0.811 0.917 7 0.754 0.875 8 0.707 0.834 9 0.666 0.798 10 0.632 0.765 11 0.602 0.735 12 0.576 0.708 13 0.553 0.684 14 0.532 0.661 15 0.514 0.641 16 0.497 0.623 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 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 α = 0.05 α = 0.01
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 2cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05.
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Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is1.8cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead_Width_(cm) Weight_(kg)7.2 1287.4 1679.8 2619.5 2218.7 2118.3 201 The regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.) Part 2 The best predicted weight for an overhead width of 1.8 cm, based on the regression equation, is enter your response here kg. (Round to one decimal place as needed.) Part 3 Can the prediction be correct? If not, what is wrong? A. The prediction cannot be correct…
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.