There is a calculation error.
8081 can be the sum of two perfect squares because its perfect squares are 41 x41+80x80=1681+6400. Answer=1681+6400
You count the inside squares of the figure.
The sum of the squares of the first 20 natural numbers 1 to 20 is 2,870.
It is Fermat's theorem on the sum of two squares. An odd prime p can be expressed as a sum of two different squares if and only if p = 1 mod(4)
The sum of their squares is 10.
If the regression sum of squares is the explained sum of squares. That is, the sum of squares generated by the regression line. Then you would want the regression sum of squares to be as big as possible since, then the regression line would explain the dispersion of the data well. Alternatively, use the R^2 ratio, which is the ratio of the explained sum of squares to the total sum of squares. (which ranges from 0 to 1) and hence a large number (0.9) would be preferred to (0.2).
There is a calculation error.
You need to cut up your figure into several parts in shapes for which we know how to calculate areas, such as squares, rectangles, and triangles. The area of your figure is the sum of the areas of its parts.
split 10 in two parts such that sum of their squares is 52. answer in full formula
8081 can be the sum of two perfect squares because its perfect squares are 41 x41+80x80=1681+6400. Answer=1681+6400
Sum of squares? Product?
You count the inside squares of the figure.
There are several parallelogram depending on the context. One such is that the sum of the squares on the four sides of a parallelogram equals the sum of squares on its diagonals.
The sum of the squares of the first 20 natural numbers 1 to 20 is 2,870.
It is Fermat's theorem on the sum of two squares. An odd prime p can be expressed as a sum of two different squares if and only if p = 1 mod(4)
85