SSY – the sum of squares Y can be partitioned into two parts:

SSY’ – the sum of squares predicted

SSE – the sum of squares error.

The sum of squares predicted is the sum of the squared deviations of the predicted scores from the mean predicted score.

What do you meant by Regression Line ?

A regression line is an estimate of the line that describes the true, but unknown, linear relationship between the two variables. The equation of the regression line is used to predict (or estimate) the value of the response variable from a given value of the explanatory variable.

Here, we have given that:

A sample of X and Y scores is taken, and a regression on line is used to predict Y from X.

SSY (the sum of squares Y)is 800 for the given regression line.SSY– thesum of squaresY – which can be partitioned into two parts:SSY= SSY¹ + SSESSY= 500 + 300SSY= 800SSY(thesum of squaresY) is 800.sum of squareshere:valueof SSY (the sum of squares Y) is 800.## Partitioning the Sums of Squares in Regression

squareddeviations of the predicted scores from themeanpredicted score.## What do you meant by Regression Line ?

explanatoryvariable.regressionon line is used to predict Y from X.valueof SSY (the sum of squares Y) is 800.Sum of Squares predicted and Sum of Squares Errors” from here: https://brainly.com/question/14056861