The next step is to calculate ANOVA and evaluate the results, which will be determined at the end of the analysis. In this instance, the original set of data will be split into two categories. Nonetheless, the primary goal of ANOVA is to test the original hypothesis and determine whether the significant differences between two data sets have a tendency to exist (Mann and Larke 544). In this instance, the mean for the first five days is (110+15+45+90+30)/5=58. In turn, the standard deviation is âˆš((110-58)2+(15-58)2+(45-58)2+(90-58)2+(30-58)2)/5=2704+1849+169+1024+784=36.12. For the second five days, the mean is (20+75+35+45+90)/5=53. The standard deviation is âˆš ((20-53)2+(75-53)2+(35-53)2+(45-53)2+(90-53)2)/5=57.4. In this case, the test reveals that Group 1 vs Group 2: Diff=-5.0000, 95%CI=-74.9402 to 64.9402, p=0.8729.
Nonetheless, the interpretation of ANOVA results is essential, as it contributes to the understanding of the differences between the groups, which are chosen for comparison (Zikmund et al. 603). In this instance, it remains evident that the differences between the variables have a tendency to exist. It could be said that the primary reason for the significant differences between the variables lays in the complexity of the prepared meals, as it remains evident that it took me longer to prepare dishes during some days. In the end, it remains evident that the complexity of the dishes and my desire to cook them defines the time, which is required for the preparation of the dishes in general. Lastly, it is clear that the external factors such as the availability of the cooking facilities and ingredients, and desire to cook complex dishes contribute to the definition of time, which is required to cook the particular dish successfully.
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