Question

credit score and interest a linear tuls significance. hdicates it iuuals are normally llUr a linear relation exists between nfidence interval about the slope of the Circumference A pediatrician wants to score and interest rate at the a 0.05 level of sign,truct a 95% in o east-squares regression lin eight versus Head Use th follow ation that may exist between a child's hei ermine the d heaice, measures their heights a (a) T She randomly selects 11 children from () etera circumferenceS ght heights and head circumferences, de actice s the following data Head Height inches), x umference (inches),x Circumference Height (e) I Head (inches),y (inches), y 17.5 17.1 17.1 17.3 16.9 17.6 26.5 27 26.75 26.75 27.5 27.75 17.3 17.5 17.3 24.5 15, dat 17 27.75 Denise Slucki, student at Joliet Junior College sults from Problem 19 in Sction 4.2 to answer the following questions: height as the explanatory variable, x, determine the le estimates of Bo and B ompute the standard error of the estimate, se e whether the residuals are normally distributed. (d) Determine sb e) If the residuals are normally distributed, test whether a linear relation exists between height and head circumference at the ?-_ 0.01 level of significance confidence interval about the slope of the true least-squares regression line (f) If the residuals are normally distributed, construct a 95% he height to be 26.5 inches. However, the child is being rather uncooperative, so the nurse is unable to measure the head circumference of the child. What would be a good estimate of this child's head circumference? Why is this a good estimate? (g) A child comes in for a physical, and the nurse determines his 10? th urricanes The following data represent the maximum wind in knots) and atmospheric pressure (in millibars) for a 14. H

Answer 1

Given that X - Height (inches) Given that X - Height (inches) Y - Head circumference a) To find beta_1 beta_1 = sigma xy - sigma x sigma y/n/sigma x^2 - (sigma x)^2/n where sigma x = 291 sigma y = 1906 sigma xy = 5044.425 sigma x^2 = 7710.25 sigma y^2 = 3303.06 5044.425 - 291 times 190.6/11/7710.25 - (291)^2/11 beta_1 = 0.183 also beta_0 = y - beta_1 x = sigma y/n - beta_1 sigma x/n 190.6/11 - 0.183 times 291/11 beta = 12.493 therefore the eqn is y = 12.493 + (0.183)x \r\n b) std error of the estimate S_e = squareroot SSE/n - 2 To find this SSE = sigma(Yi - Yi)^2 = 0.0824 therefore Se = squareroot 0.0824/11 - 2 Se = 0.0957 \r\n C) To check residuals are normally distributes or not we use normal probability plot to plot this 1) C_1, C_2 Ellipsis Cn are the residual rank them in increasing order