a. 
The estimated parameters, b_{0} and b_{1}, are minimized. 

b. 
The error terms are as small as possible. 

c. 
The largest error term is as small as possible. 

d. 
The estimated parameters, b_{0} and b_{1}, are linear. 

a. 
a constrained nonlinear optimization problem. 

b. 
a goal programming problem. 

c. 
a linear programming problem. 

d. 
an unconstrained nonlinear optimization problem. 
R ^{2} is calculated as
a. 
RSS/ESS 

b. 
ESS/TSS 

c. 
1 − (RSS/TSS) 

d. 
RSS/TSS 
a. 
(−1 ≤ R^{2} ≤ 1) 

b. 
(0 ≤ R^{2} ≤ 1) 

c. 
(0 ≤ R^{2} ≤ .5) 

d. 
(−1 ≤ R^{2} ≤ 0) 
In regression terms, the 'line of best fit' means that the error terms are as small as possible.
This means that we must minimize the value of the cost function, so the error terms are as small as possible.
The problem of finding the optimal values of b _{0} and b _{1} is a linear programming problem.
It is essentially solving for values of b0 and b1 linearly.
R ^{2} is calculated as RSS/TSS.
R squared is equal to the ratio of the Sum Squared Regression and the Sum Squared Total; that is SSR and SST respectively.
The correct range for R ^{2} values is (0<= R ^{2} <=1).
The values of R squared are always withing this range.
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