8 only 1) pdf Dr Sealth a belogy professor at Boadlord University, las decided t

Question

8 only 1) pdf Dr Sealth a belogy professor at Boadlord University, las decided to give his classes a standardized biology exam that is nationally normed. This indicates that the normal distribution is an appropriate approximation for the probability distribution of students' scores on this exam. The probability of students' scores on this standardized exam can be estimated using the normal distribution shown below. 1. State the mean of the distribution of the biology exam scores 2. State the standard deviation of the distribution of the biology exam scores Grading Curve option originally, Dr Smith decides to curve his students exam grades as follows. Students whose scores are at or above the 90 percentile will receive an A. Students whose soores are in the 80 -89 percentiles will receive a B. students whose scores are in the70 -79 percentiles will receive a C. Students whose scores are in the 60-69 percentiles will receive a D. Students whose soores are below the 60 percentile will receive an F. 3. Find the z-scores that correspond to the following percentiles. percentile 80 percentile 70 percentile 60 percentile 4. Using that information, find the exam scores that correspond to the curved grading scale. Assume that the exam soures range from 0 to 100. (Round to the nearest whole number) -100 0-

Explanation / Answer

Grading option I is fairer than option II.Usually in this case(marks) follow normal distribution,where position of the value does matter.Another reason is that there may be some outliers.Therefore,mean is not the correct representative of the data.On the other hand,mean is considered as the measure of grades which may give incorrect conclusions in the result of the student due to bias.In normal distribution percentiles play a vital role.Again,there is no impact of outliers.In the second grading option,standard deviation is also considered as a decision maker of grades,which will give wrong conclusions due to uncertain variations.

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