The set of X1, X2, ..., XN is called X. Given that mean(X), is the sum of all X divided by N, the variance of X is mean((Xi - mean(X))2). The standard deviation of X is the square root of the variance.
You use the z-transformation.For any variable X, with mean m and standard error s,Z = (X - m)/s is distributed as N(0, 1).You use the z-transformation.For any variable X, with mean m and standard error s,Z = (X - m)/s is distributed as N(0, 1).You use the z-transformation.For any variable X, with mean m and standard error s,Z = (X - m)/s is distributed as N(0, 1).You use the z-transformation.For any variable X, with mean m and standard error s,Z = (X - m)/s is distributed as N(0, 1).
The mean of a proportion, p, is X/n; where X is the number of instances & n is the sample size; and its standard deviation is sqrt[p(1-p)]
no it is not possible because you have to take the square of error that is (x-X)2. the square of any number is always positive----------Improved answer:It is not possible to have a negative standard deviation because:SD (standard deviation) is equal to the square of V (variance).
z = (x - u)/(standard dev)The z score expresses the difference of the experimental result x from the most probable result u as a number of standard deviations. The probability can then be calculated from the cumulative standard normal distribution. ie sigma(z)
5.8 × 106 written in standard notation is 5,800,000
5.37 106 in standard form is 5.37 x 106
1.4 x 106 written in standard notation is 1,400,000
8.916 106 (8.916 x 106) in standard form is 8,916,000
actually 2.75 x 106^6
6.07 x 106 in standard form is 6,070,000
3450000
2.0 × 106 written in regular notation is 2,000,000
2 x 106
6.199219 x 106.
Expressed in standard form, 455000 is equal to 4.55 x 106.
7 million in standard notation is 7.0 x 106