414 CHAPTER 12 MULTIPLE LINEAR REGRESSIONThese plots indicate that the expected change in Ywhen x 1 is changed by one unit (say) is a
function of both x 1 and x 2. The quadratic and interaction terms in this model produce a mound-
shaped function. Depending on the values of the regression coefficients, the second-order
model with interaction is capable of assuming a wide variety of shapes; thus, it is a very flex-
ible regression model.12-1.2 Least Squares Estimation of the ParametersThe method of least squaresmay be used to estimate the regression coefficients in the mul-
tiple regression model, Equation 12-2. Suppose that nkobservations are available, and let
xijdenote the ith observation or level of variable xj.The observations areIt is customary to present the data for multiple regression in a table such as Table 12-1.
Each observation (xi 1 , xi 2 ,p, xik, yi), satisfies the model in Equation 12-2, or(12-7)The least squares function is(12-8)We want to minimize Lwith respect to 0 , 1 , p , k. The least squares estimatesof 0 ,
1 , p , kmust satisfy(12-9a)and(12-9b)Simplifying Equation 12-9, we obtain the least squares normal Equations(12-10)Note that there are pk1 normal Equations, one for each of the unknown regression
coefficients. The solution to the normal Equations will be the least squares estimatorsof theˆ (^0) a
n
i 1
̨xikˆ (^1) a
n
i 1
̨xikxi 1 ˆ (^2) a
n
i 1
xikxi 2 p ˆk (^) a
n
i 1
xik^2 a
n
i 1
xikyi
o o o o o
ˆ (^0) a
n
i 1
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n
i 1
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n
i 1
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n
i 1
xi 1 xik a
n
i 1
xi 1 yi
nˆ 0 ˆ (^1) a
n
i 1
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n
i 1
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n
i 1
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n
i 1
̨yi
L
j
`
ˆ 0 ,ˆ 1 , p, ˆk
(^2) a
n
i 1
ayiˆ 0 a
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j 1
ˆj xijb xij 0 j1, 2,p, k
L
0`
ˆ 0 ,ˆ 1 , p , ˆk (^2) a
n
i 1
ayiˆ 0 a
k
j 1
ˆjxijb 0
L a
n
i 1
̨
2
i^ a^
n
i 1
̨ayi 0 a
k
j 1
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2
0 a
k
j 1
jxiji i1, ̨ 2,p, ̨n
y ̨i 0 1 xi 1 2 xi 2 pkxiki1 xi 1 , ̨xi 2 ,p, xik, ̨yi 2 , i1, 2,p, ̨n and nk
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