The sum of the squares of the first 20 natural numbers 1 to 20 is 2,870.
The sum of the squares of the first 100 natural numbers [1..100] is 338350, while the sum of the first 100 natural numbers squared is 25502500.
88
The sum of the first 57 natural numbers is 1,625.
The sum of the first 10 natural numbers is 51.
The sum of the first 200 natural numbers is 20,001.
The sum of the squares of the first 100 natural numbers [1..100] is 338350, while the sum of the first 100 natural numbers squared is 25502500.
The sum of the squares of the first 1000 positive odd integers (from 12 to 19992) is 1333333000.
The sum of the first N square numbers is: N*(N+1)*(2N+1)/6 So putting N = 20 gives 2870.
88
The sum of the first 100 natural numbers is 5,001.
The sum of the first 57 natural numbers is 1,625.
The sum of their squares is 10.
The sum of the first 50 natural numbers is 1,251.
To get a list of the squares of the first 1000 numbers we can do:> [n^2 | n sum [n^2 | n
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).
The sum of the first 10 natural numbers is 51.
The sum of the first 200 natural numbers is 20,001.