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 272.8 mm. How does theresult compare to the actual height of 1776 mm?Foot LengthHeight282.3 277.7 252.7 259.0 278.8 258.2 273.8 262.2 O1784.9 1771.4 1675.7 1646.4 1858.9 1710.2 1789.3 1737.4.....The regression equation is y =+(x.(Round the y-intercept to the nearest integer as rded. Round the slope to two decimal places as needed.)%3DActivate WindeMSHelp me solve thisView an exampleGet more help -P Type here to search耳hpf8fgf10f11f12insertprt scdeletef2f4f5144?米esc&backspace2.34EYGH.K00%24%23

Excel Procedure: Enter x and y in Excel>Data>Data Analysis> ‘Regression’>Select y under…

Answered: Listed below are foot lengths (mm) and…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.271 b=39.101 ²-0.407044 r=-0.638 Assume the correlation is significant, and use this to predict the number of situps a person who watches 14 hours of TV can do (to one decimal place) Aft
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=a+bx b=-0.66 a=25.376 r²=0.390625 r=-0.625 Use this to predict the number of situps a person who watches 2.5 hours of TV can do. place. Round to one decimal
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 2.1 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) 8.1 7.6 8.9 7.7 8.6 9.8 Weight (kg) 156 166 213 138 197 251 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y = (Round to one decimal place as needed.)
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) Weight (kg) 7.8 175 7.3 9.5 274 7.4 156 9.9 294 9.2 183 256 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 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 1.8 cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) 7.3 7.4 9.8 9.5 8.8 8.5 Weight (kg) 152 187 286 247 237 231 The regression equation is y =+ (x. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.)
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.
Data was collected on the number of pairs of shoes people own and the number of miles they walk per week. 2 = 0.38 . Then 38% of the variation in miles walked can be explained by the linear relationship between the in the number of shoes owned and the miles walked. 62% is attributable to other factors. false true
Find the regression equation, letting the diameter be the predictor (x) variable. Find the best predicted circumference of a marble with a diameter of 1.9 cm. How does the result compare to the actual circumference of 6.0 cm? Use a significance level of 0.05. Baseball Basketball Golf Soccer Tennis Ping-Pong Volleyball 5 Diameter 7.4 23.6 4.3 21.7 7.1 3.9 21.5 Circumference 23.2 74.1 13.5 68.2 22.3 12.3 67.5
2
Find Karl person's coefficient of correlation using regression coefficient byr=-0.56, bry=-0.83 Answer:
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 2.5 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) 7.6 7.4 9.8 8.8 9.3 7.3 Weight (kg) 142 163 256 188 231 156 The regression equation is y= + x. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2.5 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? 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. B. The prediction cannot be correct because a negative weight does not make sense and because there is…

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