Question 4) The following outputs are simple and multiple regression models for on age, triglyceride, and weight of 40 subjects. X= age. Y= triglyceride, and Z = weight. The dependent variable is triglyceride The ind ent variables are age and weight. U SAS outputs answer the following questions: (each part 6 point , total 30 points) Model 1: Age on Triglyceride Variabl Parameter Estimate Label Parameter Standard Estimate Error t Value er > It] Standardized Estimate Number of Observations Interce Intercept 1 -1.06372 11.21894 -0.09 0.9250 Parameter Estimates Variable Label Parameter Standard Age 1 1.76864 0.17789 9.95 <.0001 df estimate error t value tandardized ec> It Estimate Number of Observations Intercept Intercept 1 -86.72373 31.34343 -2.77 0.0087 Used Weight 1 0.94691 0.15154 6.25 <.000 model weight on triglyceride analysis of variance age and f squares square value ex> F Number of Observations Read Model 21707 21707 99.08 <.0001 number of observations error used read analysis variance sum mean root mse r-square source square f value pc> F Analysis of Variance Dependent 0.7155 Model 15220 15220 39.0 <.0001 sum of mean adj r-sq error source df squares square f value pc> F oeff Var 13.68645 Corrected 30033 Model 2 23695 11847 <.0001 total error corrected be root mse r-square dependent mean adj r-sq coeff var r- square e what would the triglyceride someone who is years old and weight use model with age regression parameter estimates variable label estimate standard tvalu standardize intercept fit a of as predicted by prediction equation equation: triglyceride_ diction b . .44 test if your slope significant for independent on since our p value less than alpha .05 we reject null conclude that ans there linear relation different from this means relationship between triglyceride. fail to cannot no d r adjusted anova table>