For linear regression calculated for a sample of n = 20 pairs of X and Y values, what is the value for degrees of freedom for the predicted portion of the Y-score variance, MSregression?

The degrees of freedom for the model on this case is given by where k =1 represent the number of variables.

The degrees of freedom for the error on this case is given by . Since we know we can find N.

And the total degrees of freedom would be

Step-by-step explanation:

Previous concepts

Analysis of variance (ANOVA) "is used to analyze the differences among group means in a sample".  

The sum of squares "is the sum of the square of variation, where variation is defined as the spread between each individual value and the grand mean"  

When we conduct a multiple regression we want to know about the relationship between several independent or predictor variables and a dependent or criterion variable.

Solution to the problem

If we assume that we have independent variables and we have   individuals, we can define the following formulas of variation:  

And we have this property  

The degrees of freedom for the model on this case is given by where k =1 represent the number of variables.

The degrees of freedom for the error on this case is given by . Since we know  we can find N.

And the total degrees of freedom would be

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step-by-step explanation:

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pentagon

step-by-step explanation:

the coordinates   a(5, -2), b(3,-1),c(-4,-4) d(-3,8) and e(-1,4) forms the vertices of the polygon when they are connected in order.

in order to classify that which polygon is it with the given coordinates, first count the number of vertices and then count the number of sides it can make with these vertices.

since, the given coordinates have five vertices, therefore, it will make five sides. moreover, pentagon is regular in nature therefore it will be convex.

now, a polygon with five sides is known as pentagon, therefore, the given polygon is the pentagon.