Model Vs1
Model Vs1
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Regression in Statistic?
A regression model to predict the price of diamonds included the following predictor variables: the weight of the stone (in carats where 1 carat = 0.2 grams), the color rating (D, E, F, G, H, or I), and the clarity rating (IF, V VS1, V VS2, VS1, or VS2)
Choose the correct model form for predicting diamond price.
A. Price = β0 + β1Weight + β2ColorD + β3ColorE + β4ColorF + β5ColorG + β6ColorH + β7ClarityIF + β8ClarityVVS1+ β9ClarityVVS2+ β10ClarityVS1
B. Price = β0 + β1Weight + β2Color + β3Clarity
C. Price = β0 + β1Weight + β2ColorD + β3ColorE + β4ColorF + β5ColorG + β6ColorH + β7ColorI+ β8ClarityIF + β9ClarityVVS1+ β10ClarityVVS2+ β11ClarityVS1+β12ClarityVS1
Agree with B, except that anyone who knows diamonds knows that the interaction term is huge. That magic IF/D/6 carat rock is worth way more than the sum of its parts.
Anyway it's B because there is an ordering to color/clarity/and carats