more answers

hw2/answers

@@ -13,8 +13,8 @@ linear model.
 X 3 = Gender (1 for Female and 0 for Male), X 4 = Interaction between
 GPA and IQ, and X 5 = Interaction between GPA and Gender. The
 response is starting salary after graduation (in thousands of dollars).
-Suppose we use least squares to fit the model, and get β  0 = 50, β  1 =
-20, β  2 = 0.07, β  3 = 35, β   4 = 0.01, β  5 = −10.
+Suppose we use least squares to fit the model, and get β₀ = 50, β₁ =
+20, β₂ = 0.07, β₃ = 35, β₄ = 0.01, β₅ = −10.
 
 	(a) Which answer is correct, and why?
 		i. For a fixed value of IQ and GPA, males earn more on average
@@ -41,10 +41,10 @@ Suppose we use least squares to fit the model, and get β  0 = 50, β  1 =
 4. I collect a set of data (n = 100 observations) containing a single
 predictor and a quantitative response. I then fit a linear regression
 model to the data, as well as a separate cubic regression, i.e. Y =
-β 0 + β 1 X + β 2 X 2 + β 3 X 3 + .
+β₀ + β₁ X + β₂ X² + β₃ X³ + .
 
 	(a) Suppose that the true relationship between X and Y is linear,
-	i.e. Y = β 0 + β 1 X + . Consider the training residual sum of
+	i.e. Y = β₀ + β₁ X + . Consider the training residual sum of
 	squares (RSS) for the linear regression, and also the training
 	RSS for the cubic regression. Would we expect one to be lower
 	than the other, would we expect them to be the same, or is there