INTRODUCTIONThe course module; Network programming and simulation involves modeling and simulation as well as analysis of input data. Simulation is the act of implementing, testing with a model or set of models for a specific purpose, which may be one of the following: Problem solving, research, education. Modeling and simulation is a discipline that includes many branches such as: Discrete distributions, Continuous distributions, Monte Carlo modeling and simulation, Probability distributions. Modeling any system, for example a communication system, requires analysis of input data. To analyze the input data we need to introduce the use of MATLAB. MATLAB is defined as a high-level programming language and environment for mathematical calculation and design. 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 the MATLAB command window or text editor that includes the use of functions, scripts, classes, or enumerations. OBJECTIVE Using MATLAB implementations, the objective of this report is to determine , through statistical analysis, the probability distributions of the numerical data contained in the two data files provided. MATLAB which has proven to be an essential tool to use, in terms of obtaining approximate and accurate output data for the input data that will be analyzed through it. The random variables that were used for this report are going to be analyzed.HISTORY OF MATLABThe origin of MATLAB which was once known...... middle of paper ...... distribution was generated as shown below :Fig. 27 Student's t distribution using different degrees of freedomThe 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 forms curves as it rises 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 the k test is used to test a null hypothesis