INTRODUCTIONThe course module; Network programming and simulation involves modeling and simulation and also analysis of input data. Simulation is the act of implementing, testing a model or series of models for a specific objective, which could be one of the following; Problem solving, research, training. Modeling and simulation is a discipline composed of many branches such as; Discrete distributions, Continuous distributions, Monte Carlo modeling and simulation, Probability distributions. Modeling of any system, for example, communication system requires analysis of input data, to analyze input data we have to introduce the use of MATLAB.MATLAB is defined as a high level programming language and environment for calculation and mathematical conception. It can be used to analyze data and create models for a wide range of applications, including signal processing and communications, control engineering and computational finance. Generally, MATLAB application was developed around MATLAB language, most of the codes used in MATLAB are written in MATLAB command window or text editor which includes the use of functions, scripts, classes or enumerations. OBJECTIVE With the help of MATLAB implementations, the objective of this report is to determine through the use of statistical analysis, the probability distributions of the numerical data contained in the two data files provided.MATLAB which has proven to be a tool essential to use, in terms of getting approximate and accurate output data for the input data that will be analyzed through it. The random variables used for this report will be analyzed HISTORY OF MATLAB The origin of MATLAB which was once known... halfway through the paper... the distribution was generated as shown below:Fig. 27 Student's t-distribution using different degrees of freedom The figure above shows the Student's t-distribution on a curve and also shows the normal distribution with a mean of 0 and a variance of 1. T also shows how the degrees of freedom change the shape of the curves as it moves higher and when it is at its maximum degree of freedom it takes the shape of a normal curve. Where, z = normpdf(X,0,1); normal distribution curveY4 = tpdf(X,15); curve for 15 degrees of freedom.Y3 = tpdf(X,3); curve for 3 degrees of freedom.Y2 = tpdf(X,2); curve for 2 degrees of freedom.Y1= tpdf(X,1); curve for 1 degree of freedom.3. KOLMOGOROV-SMIRNOV TEST: The Kolmogorov-Smirnov test otherwise known as k test is used to test a null hypothesis