Suppose that the total sum of squares of the dataset is 1,032, and the sum of squares due to error is 76, what is the sum of squares due to regression?

The sum of squares due to regression is 956.

Step-by-step explanation:

The sources of variation while doing regression analysis are typically called Regression and Residual. Regression is the portion that can be explained by the regression equation and the Residual is the portion that is left unexplained by the regression equation.

The relation between total variation (SST), explained variation (SSR) and unexplained variation (SSE) is,

SST = SSR + SSE

It is provided that:

SST = 1032

SSE = 76

Compute the value of SSR as follows:

SST = SSR + SSE

SSR = SST - SSE

       = 1032 - 76

       = 956

Thus, the sum of squares due to regression is 956.

x = ±sqrt(10)

step-by-step explanation:

5 - 2x² = -15

subtract 5 from each side

5-5 - 2x² = -15-5

-2x^2   = -20

divide by -2 on each side

-2x^2   /-2= -20/-2

x^2 =10

take the square root on each side

sqrt(x^2 )=±sqrt(10)

x = ±sqrt(10)

check:

5 - 2x² = -15

+sqrt(10)

5 - 2(sqrt(10))² = -15

5 -2(10) = -15

5 -20 = -15

-15 = -15

-sqrt(10)

5 - 2(-sqrt(10))² = -15

5 -2(10) = -15

5 -20 = -15

-15 = -15