x+y=6x^2 + y^2=20
x=2 and y=4 or vice versa
4.
The formula for the sum of the squares of odd integers from 1 to n is n(n + 1)(n + 2) ÷ 6. EXAMPLE : Sum of odd integer squares from 1 to 15 = 15 x 16 x 17 ÷ 6 = 680
The difference.
It looks to me as if that's true. I reasoned thusly, and scratched it outon the margin of my coffee-stained notepad:You gave me integers separated by 2, so the integers are [x] and [x+2].-- Their squares are [x2] and (x+2)2-- That's [x2] and [x2 + 4x + 4].-- The sum of their squares is [x2 + x2 + 4x + 4]= [ 2x2 + 4x + 4 ]-- Since [x] is an integer, each term in that trinomial is an integer.-- The coefficients are '2' and '4', so each term is an even number.-- So their sum is even.-- Q.E.D.
It is: 4+9+25+49+121 = 208
There is no single number here. The two seed numbers are 5 and 6; their squares sum to 61.
5
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).
Then the number is 16
2
You square each number and multiply that by the frequency with which that number appears. You then sum together these results.
The sum of the squares of the digits of 13 is 12 + 32 = 10. The sum of the squares of the digits of this result is 12 + 02 = 1. Because this process results in a 1, this number is a happy number.
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)
35.
Ramanujan's number - 1729,which is the sum of squares of three numbers
The sum of their squares is 10.
The mean sum of squares due to error: this is the sum of the squares of the differences between the observed values and the predicted values divided by the number of observations.