Which statement best explains whether an inverse variation function is the best model for the data? An inverse function is the best model because as x increases, y decreases. An inverse function is the best model because the products of corresponding x- and y-values are equal. An inverse variation function is not the best model because data points approximate a straight line. An inverse variation function is not the best model because the data points show an exponential decay.

do you have the measurements?

δabc ≅ δdcb by sas because

db ≅ ac . . given (side)

bc ≅ bc . . reflexive property (side)

∠dbc ≅ ∠ acb . . alternate interior angles where transversal bc crosses parallel lines bd and ac (included angle)

6mm^3 or .6cm^3

      216

      /   \

  36     6

  /   \     /

6     6   6

factor trees are nice.

for this case we must apply distributive property to the following expression:

the distributive property states that:

then, when applying the property we have to multiply the -2 for each term within the parenthesis: