Question

these added photos correspond with the origional question and where it states “if neccessary refer back to last excercuise...” these questions are those previous questions

fach yln he following questions cessary, reer back to the last esise for a neinder abou the n the model, to w n tble below, which ma The previeus eseise considered a model that predicts a newborm's weight sing several pdctors Use de r Ivalae Pr A4ol 00000 1526 Katimate Std Errer (latercept) 1435 80 41 0,03 044 peslation n001. 333 1.1% 3 9 parity 10 09170 4.01 age 11s 027 height 199 0047 881 0.0000 waight 0.05 095 4.40 smoker a) Detammine which vaiables, if any, do not have a sigicant linar relationahip with the otome and should be andide r oval frm the deType "on ifno variebles shold be reoved ved ved from the model. Type eio variable should be should be of be model with the epe varible removed Dermine if y her ariables) ry teble below abows the re b)The Eatimate Sd. Errrvalse Pr -514 000004 15.28 0.0000 310 0.0020 S6 0.000 2.00 00459 letercept)e 80 64 14.04 gestations parity -3.29 LIS beight weicht -L82e0000- 095 smk 838 In the last two problems, approach to modeling infant weights is to consider all possibly related variables at once. Other variables of interest include length of pregnancy in days (gestation), mother's age in years (age), mother's height in inches (height), and mother's pregnancy weight in pounds (weight). Below are three observations from this data set. we considered the variables smoke and parity, one at a time, in modeling birth weights of babies. A more realistic bwt gestation parity height age weight smoke 1 120 284 27 62 100 2 113 282 33 64 135 .. ww 1236 117 297 38 65 129 0 The summary table below shows the results of a regression model for predicting the average birth weight of babies based on all of the variables included in the data set. Estimate Std. Error value Prlt) (Intercept) gestation -80.41 14,35 -5.60 0,0000 0.44 0.03 15.26 0.0000 parity -3.33 1.13 -2.95 0,0033 age -0.01 0.09 -0.10 0.9170 height 1.15 0.21 5.63 0.0000 weight 0,05 0.03 1.99 0.0471 smoke -8.40 0.95 -8.81 0.0000 a) Write the equation of the regression line. bwt x gestation + x parity + age + x height + x weight + x smoke a) Write the equation of the regression line. bwt gestation + parity + height age + weight x smoke X b) Interpret the coefficient for gestation: The model predicts a ounce increase in the birth weight of the baby for each additional day of pregnancy, all else held constant. c) Interpret the coefficient for age: ounce decrease in the birth weight of the baby for each additional year in mother's age, all else The model predicts a held constant. e) The coefficient for parity is different than in the linear model in the previous exercise. Why might there be a difference? Parity might be correlated with one of the other variables in the model, which introduces collinearity and complicates model estimation. Parity is not significant and should have been removed from the multiple regression model f) Calculate the residual for the first observation in the data set. The actual birth weight of the baby ounces. (Round to the nearest tenth.) ounces. (Round to the nearest tenth.) The predicted birth weight of the babyi ounces. The residual =

Answer 1

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Previous answers :

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