A university administrator conducted a multiple regression analysis to predict a student's COLL_GPA as a function of their SAT quantitative score, HS_GPA, the number of hours they study per day (HRS_STUDY), and the number of hours they use a cell/mobile telephone per day (HRS_CELL). Use the two partial ANOVA tables from the regression analysis below to determine how much additional variation (as a percentage) in COLL_GPA can be explained by HRS_STUDY and HRS_CELL, together, over-and-above SAT and HS_GPA: MODEL 1: COLL_GPA = f(SAT, HS_GPA) SST = 20.3 SSE = 13.1 MSR = 3.6 MODEL 2: COLL_GPA = f(SAT, HS_GPA, HRS_STUDY, HRS_CELL) SST = 20.3 SSE = 7.5 MSR = 3.2 2a. 7.59% b. 12.50% c. 36.95% d. 42.75%

15 there is a line that includes the point 0,10 and has a slope of 7/4. what is it’s equation in slope intercept form

Runners run around the school's football field. the cross country coach determines that the width of the field is seventy yards shorter than the length. part a write an expression that represents the perimeter of the football field. let x represent the length of the football field. include parentheses in your expression. next, write an equivalent expression that does not include parentheses. what property or properties did you use to simplify? explain. part b the cross country coach later determines that the length of the football field is 120 yards. all students must run five laps. using your answer from part a, determine the actual number of yards that each athlete must run in the tryouts. in order to make the team, students must complete the laps in 6 minutes. how quickly must they run each lap?

Given the equation negative 4 square root x minus 3 equals 12