Basically I have a process that belongs to a certain state at any point of time. However, the time intervals are not fixed (in fact they are exponentially distributed). Essentially, the data points reflect when the state of the process changes from one value to another. For example, the dataset below illustrates that the process is in state 0 until 2.0199, then it's in state 1 until 3.3544 and so on and so forth.
data={{0.0, 0}, {2.0199, 1}, {3.3544, 0}, {6.2484, 1}, {7.0204,
0}, {16.6974, 1}, {17.4653, 0}, {33.1508, 1}, {33.5897,
2}, {36.3656, 1}, {48.2725, 2}, {57.1227, 1}, {67.6013,
0}, {69.2908, 1}, {72.8626, 0}, {86.6029, 1}, {87.6669,
0}, {120.7927, 1}, {122.8568, 0}, {125.4026, 1}, {131.7756,
0}, {132.8221, 1}, {135.0257, 0}, {140.9808, 1}, {147.2401,
0}, {160.6539, 1}, {162.4473, 0}, {170.9659, 1}, {177.8401,
2}, {181.3527, 1}, {195.5088, 0}, {205.4589, 1}, {211.7528, 2}}
In the attempt to analyze this data (distribution, pdf, histograms, mean, variance, mean time when the state is greater than 2), I tried to follow the example on mathematica's website
RandomFunction[QueueingProcess[3, 5], {0, 15}]
Histogram[data, Automatic, "PDF"]
However, I found out, (RandomFunction and Histograms) that this histogram is really not representative of time, thus meaningless.
In short, I'm trying to find ways/examples to analyze continuous data. I do have control over the presentation of the input, so if needed, I can change that, I just don't think it would be feasible to break the data down into fixed intervals because, as you would see below some intervals may be 0.002 and others 200.00.