For years researchers have been facing difficulties when it comes to analyzing figures with more than one dimension, and most data they analyze is from one point of view. The researcher chose MANOVA to show its applicability in the stimulation analysis of data during the study. The figures obtained from multiple responses have several variables such as average time system and number awaiting demand that should be considered during data analysis. MANOVA was the best approach to help researchers analyze data accurately.
Data must be analyzed from different points of view, and a single measure is not effective because it limits the study results, and more aspects are not discovered. MANOVA can be used anytime by the experimenters because it has many benefits to the researchers (Friedman, 1985). Researchers now have an easy time handling data from multiple responses, which involves different variables through MANOVA. The problem researchers have been experiencing while analyzing data obtained from numerous reactions is now solved. The approach enhances significance levels and at the same time enable researchers to explore different ways through which they can understand how responses behave when put together.
The test done by the author was the most appropriate in the current world where more different experiments are done, and experimenters have been experiencing challenges when analyzing data with more than one variable. The researcher will have more options when analyzing results because they will now be analyzing more than one variable from the data collected. The test by the author in the current world has provided ideas on how to analyze different variables from multiple responses. The experiment done by the author is very useful in the practical world in analyzing data analysis from more than one variable, and it has enhanced more research across various fields. With the use of the new approach for analyzing other data aspects is included in the study’s final results, apart from focusing on the study’s objectives, researchers can discover more through analyzing data by the use of MANOVA.
The author used several figures to display the results obtained from the new approach after analyzing different variables from multiple responses. The author has used a total of three tables in the study. The first table represents the type of experimental design used by the author to analyze the data from different variables. The chart is very clear, and it indicates how the data was processed using the new approach of analyzing data (Friedman, 1985). The second table represents the outcomes obtained after using the further method of data analysis that is MANOVA. Table three indicates the results generated from form three different measures and the measure only concentrated on one aspect of the study. The other two factors that did not interfere with the investigation were not looked at when analyzing data and they were ignored in the interpretation of the final results.
Although the author has analyzed the results represented on the table, the interpretation is very shallow. It looks like the result tables are standing alone without proper understanding to the audience. The researcher addressed many audiences who have been suffering to do data analysis from multiple responses. It was good to provide deep explanation to enable the audience to understand the new approach. From the table alone, it is not easy to interpret the final results, and it was good for the author to explain each table for easy understanding.
Reference
Friedman, L. W. (1985). On the use of MANOVA in the analysis of multiple-response simulation experiments. In Proceedings of the 17th Conference on Winter Simulation (pp. 140-142).