Page < 9 of 15 --------- Next, age was broken into categories (18-29, 30-49, 50-64, and 65+ years). These categories were stored in Age_Category, a factor variable. The output of this model is presented in Output B. Call: Output B: Proportional Odds Model with Age as a Categorical Variable polr(formula = Feel.guilty.about. FW.fac Age_Category, data = data) Coefficients: Value Std. Error t value Age_Category30-49 -0.02552 0.2779 -0.09181 Age Category 50-64 -0.07085 Age Category65+ -0.66795 0.2755 -0.25714 0.2691 -2.48252 Intercepts: Value Std. Error t value 12 -1.9862 0.2380 -8.3460 213 -1.2235 0.2223 -5.5041 314 -0.3722 0.2156 -1.7263 Residual Deviance: 1072.893 AIC: 1084.893 - ZOOM + (c) (4 points) In this fitted model, which coefficients are significantly different from 0? Do not perform formal hypothesis tests, but please quote p-values (or p-value ranges, if you are using tables) that back-up your claims. Explain what your results mean in the context of this problem. (Note: You do not need to interpret the intercepts.) Rawr. ---------