>> hsb_full.Im > hsb_red.Im > anova(hsb_full . Im) >> anova(hsb_red. Im, hsb_full. Im) Analysis of Variance Table Analysis of Variance Table Response: math Of Sum So Mean Sq F value Pr(>F) Model 1: math ~ ses + race ses 2 1307.1 653.55 8.3945 0.0003221 * * race 3 1296.5 432.16 5.5509 0.0911350 ** Model 2: math ~ ses + race + ses:race ses: race 6 225.6 37.60 0.4829 0.8205614 Res. Df RSS Df Sum of Sq F Pr(>F) Residuals 188 14636.6 77 .85 194 14862 N H Signif, codes: 0 .*#' 0.001 .* *' 0.01 ."' 0.05 '. ' 0.1 ' ' 1 188 14637 6 225.59 0. 4829 0.8206 7. Above are the summaries of two different calls to the anova() command. Which of the following tests are significant? Circle all that apply. Test for main effect of ses Test for main effect of race Test for interaction between ses and race. 8. Which of your answers to problem 7 could you have seen from the summary ( ) output of the full model? Which of these could you have seen in the summary () output of the reduced model? 9. How many subjects were in the study? How did you determine this