Julia wants to study how the lifetime of a candle (how long it burns for) is affected by the amount of a certain fragrance oil it contains. She randomly selects 80 candles of the same size from her manufacturing unit, and records each of their lifetimes in hours) and the volume of the oil (in mL). A scatterplot and least squares estimate for the data is depicted below. O 8 . 7 0/ ооо оо o 00 6 T O Oo O B 808 Po Lifetime o O o o 1 o o O OO O M o O o 0o ps O O 4 o O O o O O O 3 10 11 12 13 14 15 Fragrance oil Computational output indicates that R2 = 0.6627 Adam wants to study the effect of a different variable- paraffin wax on the lifetimes of candles. He randomly selects 80 candles of the same size, and records each of their lifetimes in hours) and the weight of paraffin (in grams) சேகரி TO 8 Lifetime 6 900 po a 00 10 11 12 13 14 15 Paraffin Computational output indicates that R? -0.7574 Select all of the following statements that are true Select one or more It's not appropriate to use a least squares estimate for a linear regression model to describe the relationship between candle wespan and paraffin wax weight, since the relationship is non-linear. 0.6.6627% of the varation in candle lifetime is predicted by the linear relationship with tragrance oil volume, based on Julia's model c. For the two models, the response variable is candle lifetime, and the explanatory variables are the volume of fragrance oil and the paraffin wax weight respectively Od Since Adam's model has a higher value, we will make more accurate predictions of candle lifetime using the least squares estimate from his data than Julia's