question 1

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. Lifetime I I I I I I 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). Lifetime Pfm n Computational output indicates that R2 = 0.7574. Select all of the following statements that are true. Select one or more: a. It's not appropriate to use a least squares estimate for a linear regression model to describe the relationship between candle lifespan and paraffin wax weight, since the relationship is non-linear. b. Since Adam's model has a higher RZ value, we will make more accurate predictions of candle lifetime using the least squares estimate from his data than Julia's. c. 66.27% of the variation in candle lifetime is predicted by the linear relationship with fragrance oil volume, based on Julia's model. d. 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