Matlab problem Assignment Example | Topics and Well Written Essays - 1000 words

Matlab problem - Assignment ExampleThe address encipher done in matlab contains the models to implement the linear regression functions (Martinez & Martinez 39). In the general equation y = a1x + a0, y is replaced by PV, x by indicator and variable a, by aA. This gives the relationship in the midst of the X-axis and the Y-axis (Seber and lee side 63). The three expected output results be scattered dots for data output, one line for regression and banal variance, one line for standard deviation and the third line for regression line ofThe first pure tone of developing this system involves the identification of the variables to use in the regression analysis. In this computer programme, the two variables identified are PV and Indication of the solar irradiation. The next step is to develop models for linear regression to determine the relationship surrounded by the dependent and the independent variable (Chatterjee and Hadi 57). The third step is to develop a matlab source co de load containing the model and able to access the source of data to be analysed. The fourth step is to test the program and remove errors.Since the source code has been developed in matlab software, testing is done by executing the linearregression.m script. If both error is found to prevent the output from appearing, necessary correction is done in the source code (Weisberg 49).The range of the y axis was between 5 and 50 while the x axis was hard-boiled from 4 to 24. The results were productively displayed as expected and all the three lines were drawn by the program. The standard deviation for the two variables is 2.34. This indicates that the two variables deviated from the actual mean by a difference of about 2.34.The program was successful in implementing the linear regression between the two variables (Gro 42). It revealed that there is a positive correlation between PV output and the indication of solar irradiation. The scatered dots generate the best fit represented b y the regression