Normalization of a decimal value

Question

My problem is like this: I need to generate a random number from Gaussin/Norma distribution and create a histogram of a width of 0.1.

class Gaussian
{
    public static double Next(Random r, double mu = 0, double sigma = 1)
    {
        var u1 = r.NextDouble();
        var u2 = r.NextDouble();

        var rand_std_normal =   Math.Sqrt(-2.0 * Math.Log(u1)) *
                                Math.Sin(2.0 * Math.PI * u2);

        var rand_normal = mu + sigma * rand_std_normal;

        return rand_normal;
    }
}

I am using the above function to generate a Gaussian random value.

Now, in order to create a histogram, I am in need of such a calculation that is able to automatically convert the gaussian value into an array index. Something like the following:

    static void Main(string[] args)
    {
        const int N = 1000;
        int time = N;
        const double binsDistance = 0.1;
        int binsCount = (int)(N * binsDistance);

        Random rand = new Random();
        int[] array = new int[binsCount];
        int side = 0;
        for (int i = 0; i < time; i++)
        {
            double gauss = Gaussian.Next(rand);
            int binNo = Normalization.Normalize(0, binsCount - 1, gauss);
            array[binNo]++;
        }
    }

For that, I tried two calculations.

1. the first one is here.
2. the second one is here.

The problem with the first one is, it can't handle negative numbers properly.

The problem with the second one is, it is generating too many zero values.

So, I have two questions:

1. What is the basic difference between #1 and #2?
2. How can I achieve what I am trying to do?