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1.2 Agression Analysis Test

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1.2 Statistical Testing A –Correlation check between the Start measure and End measure for those given Calcium. When running a simple scatterplot diagram so as to visually check the correlation of start and end measure for calcium, it would appear that there is no obvious correlation. Calcium Correlationsa Starting Value End Value Starting Value Pearson Correlation 1 .602 Sig. (2-tailed) .065 N 10 10 End Value Pearson Correlation .602 1 Sig. (2-tailed) .065 N 10 10 a. Treatment type = Calcium When running a Pearson Correlation Test, the results for the two tailed test reveal that there is no correlation between the Start measure and End measure for those given Calcium, as there is no significance indicator at the end of the table. B - Correlation check between the Start measure and End measure for those given placebo. Again, using a simple scatterplot diagram first, to check if there is a visual linear correlation between the starting and ending values for the Placebo, it would appear that there is an amount of correlation. As there is some visual evidence of this, the author proceeded to test the strength of the correlation, by utilising the Pearson Correlation Test. Using this test, where a strong positive or negative …show more content…

In order to test the results against each other, a t-test would not be sufficient for this analysis, as this method would only allow for comparing two groups. Therefore a one way Anova statistical test could be used to test the analysis of variance. By using this, the researcher would establish whether there were any statistically significant differences between the means of the three unrelated groups of placebo, calcium and Valium. Again, for the purposes of illustration, I have run a one way Anova statistical test with the additional drug data included in the table

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