# Essay on Determining Accurate Output Data with MATLAB

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INTRODUCTION
The course module; Network programming and simulation, entails modeling and simulation and also the analyses of input data. Simulation is the act of implementing, testing with a model or a set of models for a specific objective, which might be one of the followings; Problem solving, Research, Education. Modeling and Simulation is a discipline which consists of many branches such as; Discrete distributions, Continuous distributions, Monte Carlo modeling and simulation, Probability distributions. Modeling any system, for example, communications system requires the analysis of the input data, to analyze the input data we have to introduce the use of MATLAB.
MATLAB is defined as a high-level programming language and an environment for Mathematical calculation and conception. It can be used to analyze data and create models for a large scope of applications, which includes signal processing and communications, control engineering and computational finance.
Generally, the MATLAB application has been developed around the MATLAB language, most of the codes used in MATLAB are written in the MATLAB command window or the text editor which includes the use of functions, scripts, class or enumeration.
OBJECTIVE
With the aid of MATLAB implementations, the objective of this report is to determine through the use of statiscal analysis, the probability distributions of the numerical data contained in the two data files given.
MATLAB which has been proven to be an essential tool to use, in terms of getting approximate accurate output data for the input data which are going to be analyzed through it. The random variables which were used for this report are going to be analyzed
HISTORY OF MATLAB
The origin of MATLAB which was once know...

... middle of paper ...

... distribution was generated as shown below:

Fig. 27 Student’s t distribution using different degrees of freedom
The above figure shows the student 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 shapes 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 curve
Y4 = 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 verify a null hypothesis

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