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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

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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

[tex]\sum x=90[/tex]

[tex]\sum(y-\bar y)(x-\bar x)=466[/tex]

[tex]\sum y=170[/tex]

[tex]\sum(x-\bar x)^2=234[/tex]

n=10

[tex]\sum(y-\bar y)^2=1434[/tex]

SSE=505.98

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

It means we have to find SSR.

SST=[tex]\sum(y-\bar y)^2=1434[/tex]

[tex]SSR=SST-SSE=1434-505.98=928.02[/tex]

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

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