Developing A Software Package to Detect and Address the Autocorrelation Problem

Autocorrelation is a common challenge in regression analysis. It can lead to biased and inefficient parameter estimates, ultimately affecting the accuracy of the results. Many statistical software packages, such as SPSS and Minitab, include features for identifying Autocorrelation, such as the graphical method and the Durbin-Watson test. However, these methods have drawbacks. The graphical method relies on subjective interpretation, and SPSS lacks the necessary information to interpret the significance of the Durbin-Watson test results. Java-based software has been developed to address these limitations and tackle the problem of Autocorrelation in regression analysis. The software utilizes the Durbin-Watson method, providing a more precise alternative to graphical methods. It presents analysts with an intuitive interface. The software has been tested using accurate data, and the results validate its ability to detect and address positive Autocorrelation, consistent with previous findings on the Dataset. This software enhances the dependability and precision of regression analysis in different domains. The software can be further enhanced by expanding its scope to include more complex regression models, adding other methods to address Autocorrelation, and improving the user interface for greater ease of use. Keywords: Simple Liner regression, Autocorrelation, Durbin-Watson, Object-oriented programming.