Answer:
D. 6
Step-by-step explanation:
⇒The question is on Sum of Squared Errors(SSE)
⇒A linear regression equation is one inform of Y= a +bx where Y is dependent variable and x is the independent variable where as b is the slope.
⇒SSE for a set of data is the sum of squares of the calculated residuals where a residual/error is a deviation of a point from that in the line of best fit.
⇒General expression; y₁-y₀ where y₁ is the point out of the line and y₀ is the point in the line of best fit.
⇒ y₁-y₀ =ε₀................where ε is epsilon
⇒sum= (ε₀)² + (ε₁)² + (ε₂)²
ε₀= a (3,6).......A (3,7)......(6-7)² = -1² = 1
ε₁= b (6,8).......B (6,6).......(8-6)²= 2²= 4
ε₂= c (9,4).......C (9,5).......(4-5)²= -1² = 1
Sum of squares is = 1+4+1= 6
See attached graph showing the points on the line and those out of the line.
