Question

A regression and correlation analysis resulted in the following information regarding a dependent variable ( y) and an independent variable ( x). Σx = 90 Σ(y - )(x - ) = 466 Σy = 170 Σ(x - )2 = 234 n = 10 Σ(y - )2 = 1434 SSE = 505.98 ​ The sum of squares due to regression (SSR) is

Answer 1

Answer:

The sum of squares due to regression(SSR)=928.02

Step-by-step explanation:

We are given that

Dependent variable=y

Independent variable=x

$\sum x=90$

$\sum(y-\bar y)(x-\bar x)=466$

$\sum y=170$

$\sum(x-\bar x)^2=234$

n=10

$\sum(y-\bar y)^2=1434$

SSE=505.98

We have to find the sum of squares due to regression.

It means we have to find SSR.

SST=$\sum(y-\bar y)^2=1434$

$SSR=SST-SSE=1434-505.98=928.02$

Hence, the sum of squares due to regression(SSR)=928.02