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# 905 Paper

# 905 Score Report

Dear Team #905,

Most of your paper is neatly presented along with an easily understandable approach to the problem regarding the normal distribution. In terms of readability, it is strong if given more detailed explanation. However, there could be some improvements:

First, regarding format some of your special characters do not show up. Always make sure to properly convert the file. In the introduction of the model section, you are not supposed to include or place formulas. Instead, formulas and their explanations should come later in the “Clear Explanation of Models” section. Also, most of your sensitivity analysis should actually go in the “Clear Explanation of Models” section. This section will ultimately be your longest and one of your most important. Furthermore, it’s important to always label your assumptions and justifications as this sets up the parameters of your models. Also remember to consistently reference citations within your paper.

Second, when writing out explanations it’s important to restate the problem, rather than simply copying the question. Always verify your model with simulation to clearly demonstrate its methodology. When presenting papers to judges or the scientific community, one of the key aspects is ensuring they can see your model function with actual data. Keep in mind that your sensitivity analysis should focus on what will happen if the IQ distribution is slightly skewed or fails to follow a standard distribution. Its purpose is to test whether your algorithm can survive under alternative assumptions. Can it still present accurate results?

Finally, some suggestions of improvement are to expand explanations beyond standard concepts such as the normal distribution. Be sure to go more into the details of your model. Try creating a flow chart of your algorithm. Sometimes this becomes clearer than word explanations. Also try evaluating the effectiveness of your model through a set standard.

Finally, some suggestions of improvement are to expand explanations beyond standard concepts such as the normal distribution. Be sure to go more into the details of your model. Try creating a flow chart of your algorithm. Sometimes this becomes clearer than word explanations. Also try evaluating the effectiveness of your model through a set standard.

Best,

Association of Computational and Mathematical Modeling