The analysis for the IQ and GPA scores are presented in the following table: The best frequency distribution for the data is a histogram, and from the analysis, the frequencies and histogram can be indicated as:An analysis of the symmetry and skewness of the data indicates that the IQ scores are evenly distributed. there is no positive or negative skew as would be expected of qualitative data. This means that the data is not symmetrical around any point or skewed in a specific direction. Assuming that the IQ test has a mean of 100 and a standard deviation of 15, 14 students fell one standard deviation below the test mean, and 6 students fell two standard deviations below the test mean. Percentage of students with IQ≤70 = (5/30)*100%= 16.67%.Percentage of students with IQ≥100= (9/30)*100%= 30%The first assumption from the test results is that the test was academic, since the students who had high GPA scores had the highest scores for IQ. This could also be an indication of the acuity of the students. Having a correlation of .87 between the GPA scores and the IQ score means that a student who had a high GPA score is more likely to have a high IQ score. The correlation means that the IQ scores can be predicted by considering the GPA score, a high GPA score would indicate a high IQ and a low GPA would indicate a low IQ. The limitation if the study is from a rural school is that the quality of the data would be affected because the students take English as a second language. In this case, the test would not be valid because the students would fail because of the language barrier. Comparing the sample mean to a population would require the use of a z-test, because the expected sample size would be huge. t-tests would be used if the sample is small (n