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. CrunchIt! CSV Excel JMP Mac Text Minitab PC Text R SPSS TI Calc In particular, note the variables Barefoot and Footwear2. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear2 represents their range of ankle motion (in degrees) while wearing 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 medical shoes, y, based on their range of ankle motion while barefoot, x . Round your coefficients to two decimal places of precision. D= 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 medical shoes, j. Round your answer to two decimal places. Suppose Jan's actual range of ankle motion while wearing medical shoes is 9.795. Use her predicted range of ankle motion to calculate the residual associated with this value. Round your answer to two decimal places. residual = In order to assess the linear regression equation's ability to predict range of ankle motion, the physical therapist reviewed a scatterplot of the researchers' sample data and calculated the correlation, r = 0.77. Ankle mobility F= 0.77 35 15 Medical shoes and no hosiery (degrees 14 20 25 30 Barefoot (degrees) Is it reasonable for the physical therapist to assume that the least-squares linear regression line would accurately predict Jan's range of ankle motion? Yes, because the ranges of ankle motion follow a linear pattern and are moderately correlated. No, because her patient's range of ankle motion does not fall within the range used to generate the regression line. No, because the prediction can only be accurate if the correlation, r, is greater than 0.85 or less than -0.85. Yes, because the sample data does not contain outliers. No, because the sample data points do not all fall in a straight line pattern