Question

Range of ankle motion is a contributing factor to falls among the elderly. Suppose a team of researchers is studying how compression hosiery, typical shoes, and medical shoes affect range of ankle motion. Click to download the data in your preferred format. Crunchlt! CSV Excel JMP Mac Text Minitab PC Text RSPSS TI Calc In particular, note the variables Barefoot and Footwear5. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear5 represents their range of ankle motion (in degrees) while wearing compression hosiery and medical shoes. Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing compression hosiery and medical shoes, ĝ, based on their range of ankle motion while barefoot, x. Round your coefficients to two decimal places of precision A physical therapist determines that her patient Jan has a range of ankle motion of 7.26° while barefoot. Predict Jan's range of ankle motion while wearing compression hosiery and medical shoes, 9. Round your answer to two decimal places.

In particular, note the variables Barefoot and Footwear5. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear5 represents their range of ankle motion (in degrees) while wearing compression hosiery and medical shoes.

Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing compression hosiery and medical shoes, ?̂ , based on their range of ankle motion while barefoot, ? . Round your coefficients to two decimal places of precision.

Answer 1

Answer least square riegvesebus variable y and Let be - bo+b, a the y line between the dependent in dependent variable Foot wear 5 X : Bare foot least Square we According to poinciple of have esonate that Such bo and b, minimum E-, E (y; -be- 6,2; ) ? is si Now , for minina of E n -2 әЕ abo Ë (y; - bo-b/*; ) = 0 0 mo 91 = nbot bi 2, -)(1) 1:1 ӘE ab, ici -2. E ni 19; -bo-b, ;) = 0 Ê exigi a & uitb & ; ²) (2) ini ic! Known ay noomad and (2) Equa bons un equabions solue and when we shall get we the estimates of bo and 6, and b. are as follows: estimate of bo bo ý - b, where n 121 bis Ê (a; -5) (93-9) i1 § Lai-n) 2 isl

SUMMARY OUTPUT 0.5288732 0.2797068 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.2508951 4.2633642 27 ANOVA df SS MS F Significance F 0.00456414 9.70809 Regression Residual Total 1 25 176.4569137 454.4068617 630.8637754 176.457 18.1763 26 Standard Error t Stat Coefficients 13.3072741 P-value 0.00078 Upper 95% 20.4791822 3.482288856 3.82142 Lower 95% 6.13536593 0.15505602 Intercept Barefoot 0.45739559 0.146799668 3.115781 0.004564 0.75973517

above excel acel output, we the get , From Intercept bo = 13:31 slope bia 0.46 Regression equation is 1 13.31 to 462 y please like & support Thank you.