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DAT 520 Final Project Topics and Sources

The above is the decision tree that will guide this research. In this study, the student has the option to select math or science. However, although the choice may be determined by other things such as interest and passion in the course, the primary determinant that will be considered in this study is student performance.  The decision tree starts with the student, then the determining factor on whether they select math or science will depend on the aggregate points (AP) that the student acquires during that academic year. In this case, if the student scores a better AP in Math, therefore, they settle for the math option. Otherwise, if the student has a better AP in science, they will select science over math.

Documentation of Potential Complications in The Analysis Process

Some of the potential complications that may be encountered in the analysis process include; the amount of data that need to be collected and analyzed.  Another challenge will be the visual presentation of data. After the data has been computed, it needs to be visually presented in charts and graphs. Considering that the information will be pulled from two subjects and will need to be aggregated, it may be time-consuming and frustrating. The need to verify data for accuracy will be a challenge to data analysis in this study. If the data is inaccurate, there will be manual errors during data transfers and entry.

An Evaluation of Results

From the proposed model and the analysis of the results, it is indeed clear that correct results can be obtained. The reasonableness of the results will be apprehended by checking and verifying the solutions’ results from the calculation and the data inputs.  The analysis will also assess the accuracy of the results. Assessing the accuracy of the estimates will involve evaluating the potential errors from the data entry. I will counter-check the entry data to help identify any potential missing elements and follow the model guide to eliminate or identify any potential errors in data entry, analysis, and output.