Scenario Analysis & Discussion Question

ANOVA is a hypothesis testing technique used to compare the equality of means for two or more groups; for example, it can be used to test that the mean number of computer chips produced by a company on each of the day, evening, and night shifts is the same. Give an example of an application of ANOVA in an industrial, operations, or manufacturing setting that is different from the examples provided in the overview. Discuss and share this information with your classmates.

In responding to your peers, select responses that use an ANOVA application that is different from your own. Are the results of the ANOVA application statistically significant? Why are the results significant or not significant? Explain your reasoning. Consider how ANOVA could be applied to the final project case study.

Support your initial posts and response posts with scholarly sources cited in APA style.

https://statistics4beginners.wordpress.com/2015/02/18/how-to-calculate-anova-in-excel-2013/

PLEASE GIVE A 1-2 PARAGRAPH RESPONSE TO THE FOLLOWING:

1.

In this module, our goal is to learn the statistical process of comparing several population means through a procedure called "analysis of variance", or ANOVA. ANOVA uses the variance from the mean of 2 or more sample populations to see if there is a statistically significant difference between them (Sharpe, DeVeaux, Velleman, 2016). We've learned that this is a valuable tool in all sorts of areas of study, including automotive, chemical, and medical industries.

There are many practical examples of ANOVA throughout business. As previously mentioned, the medical field can benefit from the use of this statistics tool. For example, a drug company may be interested in the results of clinical trials for a few new drugs they plan to release. Medicine A, B, and C are all now in the clinical testing phase, so the instances in which each cures a specific ailment can be summed up using ANOVA. Each of the individual drugs, through the course of multiple trials, will have a number of "cured" patients. The following is an example of what the results may be, in table format:

                                     A                              B                                 C

Trial                1             4                              9                                  2

                       2             5                              8                                  7

                       3             7                              1                                  6

                       4             6                              1                                  5

                       5             6                              4                                  9       

Using ANOVA to evaluate the variance from the mean for each trial, the ultimate goal would be to compare each trial to one another. By comparing the variance, we can say, with statistical confidence, that one medicine may be more effective than the other two. This sort of analysis would help the company decide which of the medicines to push to the consumer markets.

Citations

Sharpe, N. D., DeVeaux, R. D., & Velleman, P. (2016). Business Statistics (Third ed.). Retrieved from https://view.ebookplus.pearsoncmg.com/ebook

2.

Going back to my module 3 and module 5 discussion post examples, I work within the IT department of a large company. We are forming new software development teams using agile development practices. We want to better understand if the make-up of the team has any impact to the number of defects that are produced per 1000 lines of code. We want to see If the mean number of defects per 1000 lines of code is different for a development team with a senior developer and a senior tester, one with a junior developer and a senior tester, one with a senior developer and a junior tester, and one with both a junior developer and junior tester. 

 

In order to determine if the mean number of bugs found per 1000 lines of code is different for these four types of teams, we would use ANOVA analysis and use a sampling of the last 15 projects that each type of team has worked on and we gather the number of defects per 1000 lines of codes for each of these projects. Our hypothesis is that of the four project team types at least one of the means of bugs per 1000 lines of code is different from the other teams. The first step would be to create a box plot of the results of the four teams’ bugs per 1000 lines of code. Next, we can perform an ANOVA analysis on the results using a 5% significance level. If we find that the p-value is smaller than the significance level, we can reject the null hypothesis that means of the four teams are the same. Additionally, per Statistics For Beginners (2015), "if the F > F crit, we reject the null hypothesis. The means of the…populations are not equal."

 

 

References:

 

Statistics For Beginners. (2015, February 18). How To Calculate ANOVA (ONE WAY ANOVA) IN EXCEL 2013. Retrieved from https://statistics4beginners.wordpress.com/2015/02/18/how-to-calculate-anova-in-excel-2013/