Given that cov(x,y) = 8, $s _ { y } ^ { 2 }$ = 14, $S _ { x } ^ { 2 }$ = 10 and n = 6, the value of the sum of squares for error, SSE, is 38.

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The sum of squares for error, SSE, is calculated as:SSE = n * $s _ { y } ^ { 2 }$ - b^2 * $s _ { x } ^ { 2 }$where n is the sample size, $s _ { y } ^ { 2 }$ is the variance of y, $s _ { x } ^ { 2 }$ is the variance of x, and b is the slope of the regression line.In this case, n = 6, $s _ { y } ^ { 2 }$ = 14, $s _ { x } ^ { 2 }$ = 10, and cov(x,y) = 8. The slope of the regression line can be calculated as:b = cov(x,y) / $s _ { x } ^ { 2 }$ = 8 / 10 = 0.8Therefore, the SSE is:SSE = 6 * 14 - 0.8^2 * 10 = 38

Given That Cov(x,y) = 8 sy2s _ { y } ^ { 2 }sy2​

Given That Cov(x,y) = 8 $s _ { y } ^ { 2 }$