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 273.3 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.9 278.1 253.3 259.4 279.1 257.8 273.6 262.2 Height 1785.0 1771.2 1676.2 1646.2 1858.9 1710.2 1788.7 1736.6 The regression equation is y=+ (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 273.3 mm is mm. (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? OA. The result is very different from the actual height of 1776 mm. OB. The result is exactly the same as the actual height of 1776 mm. OC. The result is close to the actual height of 1776 mm. OD. The result does not make sense given the context of the data.

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

Use the following data to estimate a regression with femur length as the x-variable and tibia length as the y-variable. Femur Length Tibia Length 18.7 14.2 20.5 15.9 16.2 13.1 15.0 12.4 19.0 16.2 21.3 15.8 21.0 16.2 14.3 12.1 15.8 13.0 14.3 13.8 18.8 18.7 Is the following statement true or false: "The intercept of the regression is equal to (approximately) 3.59" True False

Using your favorite statistics software package, you generate a scatter plot with a regression equation and correlation coefficient. The regression equation is reported asy=29.62x+64.57y=29.62x+64.57and the r=0.784r=0.784.What proportion of the variation in y can be explained by the variation in the values of x? r² = %Report answer as a percentage accurate to one decimal place.

Using your favorite statistics software package, you generate a scatter plot with a regression equation and correlation coefficient. The regression equation is reported as . 72.45х + 93.26 y = - 0.23. and the r = What percentage of the variation in y can be explained by the variation in the values of x? r2 = % (Report exact answer, and do not enter the % sign)

Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a significance level of 0.05. Right Arm 100 99 92 80 79 O Left Arm 175 169 182 149 148 E 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.)

Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a significance level of 0.05. Right Arm 100 99 91 76 76 5 Left Arm 175 170 146 147 146 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.) Given that the systolic blood pressure in the right arm is 85 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)

What type of relationship does the following regression line represent? O O O a. No relationship b. A curvilinear relationship c. A positive relationship d. A negative relationship

The data show the chest size and weight of several bears. Find the regression​ equation, letting chest size be the independent​ (x) variable. Then find the best predicted weight of a bear with a chest size of 4141 inches. Is the result close to the actual weight of 273273 ​pounds? Use a significance level of 0.05.   Chest size​ (inches) 4040 5353 3838 4343 4444 5858   Weight​ (pounds) 227227 360360 153153 206206 234234 414414   LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r.       Question content area bottom Part 1 What is the regression​ equation?   ModifyingAbove y with caretyequals=enter your response hereplus+enter your response herex ​ (Round to one decimal place as​ needed.) Part 2 What is the best predicted weight of a bear with a chest size of 4141 ​inches?   The best predicted weight for a bear with a chest size of 4141 inches is enter your response here pounds. ​(Round to one…

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…