The value of SSY (the sum of squares Y) is 800.

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.

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.

if SSY¹ = 300

SSE = 500

n = 50

we know,

SSY = SSY¹ + SSE

SSY = 500 + 300

SSY = 800

Hence,

The value of SSY (the sum of squares Y) is 800.

Learn more about “Sum of Squares predicted and Sum of Squares Errors” from here: https://brainly.com/question/14056861

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