15. Suppose IQ scores were obtained for 20 randomly selected sets of twins. The 20 pairs of measurements yield x = 100.56, y = 100.2, r = 0.930, P-value = 0.000, and y = - 5.31 + 1.05x, where x represents the IQ score of the twin born first. Find the be predicted value of y given that the twin born first has an IQ of 90? Use a significance level of 0.05. Print Click the icon to view the critical values of the Pearson correlation coefficient r. The best predicted value of y is (Round to two decimal places as needed.) 7: Critical Values of the Pearson Correlation Coefficient r Critical Values of the Pearson Correlation Coefficient r n a = 0.05 a = 0.01 4 NOTE: To test Ho: p = 0 0.950 0.990 5 0.878 against H1: p #0, reject Ho 0.959 6 0.811 f the absolute value of r is 0.917 7 0.754 greater than the critical 0.875 8 0.707 value in the table. 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 .305 80 0.220 0.286 90 0.207 0.269 100 0.196 0.25616. Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. X 11 10 14 5 9 6 12 4 8 13 y 11.14 10.85 10.31 5.26 10.30 6.94 8.34 11.14 3.31 9.45 10.86 y = x (Round to two decimal places as needed.) Create a scatterplot of the data. Choose the correct graph below. O A. O B. O C. OD. 25 HH 20 20 20-] 15 15- 15 15 10 10 103 5 360 OHHHHHH HH 10 25 5 10 1 20 25 0 5 10 15 20 25 0 5 10 15 20 25 Identify a characteristic of the data that is ignored by the regression line. O A. There is no trend in the data. O B. There is an influential point that strongly affects the graph of the regression line. O C. The data has a pattern that is not a straight line. O D. There is no characteristic of the data that is ignored by the regression line.17. Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.7 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.0 278.3 253.2 259.2 279.2 258.2 274.0 262.3 Height 1785.3 1771.4 1675.9 1646.2 1859.0 1710.2 1788.7 1737.4 The regression equation is y = 1 x. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.7 mm is |mm. Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? O A. The result is very different from the actual height of 1776 mm. O B. The result is close to the actual height of 1776 mm. O C. The result is exactly the same as the actual height of 1776 mm. O D. The result does not make sense given the context of the data. 18. Assume that you have paired values consisting of heights (in inches) and weights (in lb) from 40 randomly selected men. The linear correlation coefficient r is 0.486. Find the value of the coefficient of determination. What practical information does the coefficient of determination provide? Choose the correct answer below. O A. The coefficient of determination is 0.236. 76.4% of the variation is explained by the linear correlation, and 23.6% is explained by other factors. O B. The coefficient of determination is 0.764. 23.6% of the variation is explained by the linear correlation, and 76.4% is explained by other factors. O C. The coefficient of determination is 0.236. 23.6% of the variation is explained by the linear correlation, and 76.4% is explained by other factors. O D. The coefficient of determination is 0.764. 76.4% of the variation is explained by the linear correlation, and 23.6% is explained by other factors19. Use the value of the linear correlation coefficient r to find the coefficient of determination and the percentage of the total variation that can be explained by the linear relationship between the two variables. Print r = 0.954, where x = distance in miles and y = fare in dollars What is the value of the coefficient of determination? The coefficient of determination is 'Round to four decimal places as needed.) What is the percentage of the total variation that can be explained by the linear relationship between the two variables? Explained variation = % (Round to two decimal places as needed.) 20. Listed below are altitudes (thousands of feet) and outside air temperatures (F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet). Altitude 2 10 16 20 29 31 34 Temperature 56 36 22 - 28 - 41 - 56 a. Find the explained variation. (Round to two decimal places as needed.) b. Find the unexplained variation. (Round to five decimal places as needed.) c. Find the indicated prediction interval. OF