Deepu Kumar

public class Fibonacci {

    public static void main(String[] args) {

        int n = 10, t1 = 0, t2 = 1;
        System.out.print("First " + n + " terms: ");

        for (int i = 1; i <= n; ++i)
        {
            System.out.print(t1 + " + ");

            int sum = t1 + t2;
            t1 = t2;
            t2 = sum;
        }
    }
}

Probability density function of a continuous random variable YYish(y).h(y).

What does that mean? Now, often students are mistaken and say that this implies the probability that Y=yY=y (i.e P(Y=y)=h(y)P(Y=y)=h(y)). No, wrong!

It means, P(y−(δy)/2<=Y<y+(δy)/2)=h(y)×δyP(y−(δy)/2<=Y<y+(δy)/2)=h(y)×δy where δyδy is a very small quantity (in the limit δy→0)δy→0), which means,h(y)×δh(y)×δ = The probability that the random variable Y lies within the small strip[y−(δy)/2,y+(δy)/2))[y−(δy)/2,y+(δy)/2)). (note, the length of the region is δy.δy.)

This is the shaded area shown in the curve below.

Note:

is called the cumulative distribution function (cdf).