The dataset used for this assignment was the grades.sav data file. The variables used were gender, GPA, total and final. GPA and final were used in the histogram scales, as well as skewness, kurtosis values, and scatterplot. This assignment included a sample size of (N)105. Testing Hypotheses There are two histograms, showing GPA information and showing final grade information. Histograms are commonly used with interval or ratio level data (Corty, 2007). The GPA data is distributed and slightly skewed to the right, meaning it has positive skewness and a maximum distribution. The final histogram also has a leptokurtic frequency distribution, but is skewed to the left, meaning it has negative skew. Descriptive StatisticsN Minimum Maximum Mean Std. Deviation Skewness KurtosisStatistic Statistics Statistics Statistics Statistics Statistics Std. Error Statistics Std. ErrorGPA 105 1.14 4.00 2.7789 .76380 -.052 .236 -.811 .467final 105 40 75 61.48 7.943 -.335 .236 -.332 .467Valid N (listwise) 105A kurtosis value close to zero indicates a form close to normal. A negative value indicates a distribution that is peaked higher than normal, and a positive kurtosis indicates a flatter than normal shape. Extremely positive kurtosis indicates a distribution in which more values ​​are located in the tails of the distribution rather than around the mean (Grad pad, 2013). A kurtosis value of +/-1 is considered very good for most psychometric uses, but +/-2 is also generally acceptable (Grad pad, 2013). The chart above shows GPA with a kurtosis of -0.811; for a while the final kurtosis is -33.2. The extent to which a distribution of values ​​deviates from symmetry around the mean is skewness. A value of zero means the distribution is symmetrical, while a positive skewness indicates a greater number of smaller values, and a negative value indicates a greater number of larger values ​​(Grad pad, 2013). Acceptability values ​​for psychometric purposes (+/-1 to +/-2) are the same as with kurtosis. Scatter plots are similar to line charts in that they both use horizontal and vertical axes to plot data points. The more the data aims to draw a straight line, the higher the correlation between the two variables, or the stronger the relationship (MSTE, nd). The scatterplot above does not have a straight line formation, which shows that there is no straight line. strong relationship between the two variables GPA and final. An example of a null hypothesis for the variables used in this data collection would be: "Does GPA predict final exam scores?" An alternative hypothesis would be that GPA scores determine exam results..