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.
Simply put an ANOVA test is a way to test groups to see if there is a difference between them. From this you are able to determine if the experiment results are significant and help determine if you should accept or reject the null hypothesis. (ANOVA Test: Definitions, Types, Examples, 2018) Part of my current job is ensuring that the packaging of our auto parts can withstand transportation and make it to the dealers safely. We are currently having an issue with our bumpers and an ANOVA test could be used to confirm what method should be implemented.
We can have Group A which is the number of damages with current packaging, Group B the number of damages with the amount per package decreased, Group C the number of damages with foam blocks added, and Group D the number of damages with cardboard protection added. Based on the results we can determine which packaging improvement needs to be implemented. In some cases, not so many options are needed, but bumpers are a costly, high volume item and the packaging is costly, so many options are beneficially in this scenario.
ANOVA Test: Definitions, Types, Examples. (2018, March 8). Retrieved from Statistics How To:http://www.statisticshowto.com
When I was younger, I use to work for a company that manufactured the electrical components that went into the car dashboards. This involved using machines to cut wire, welding machines and of course other equipment to piece together the huge dashboards. One of the years when the price of the material went up, the managers of the company quickly began to audit all expenses incurred by the company. As it turned out the electricity bill had gone up. In particular for the month of July. The managers wanted to make a claim against the electric company under the premise that our manufacturing processes had not changed in the last 3 months and that a huge spike on the electric bill was due to foreign circumstances to the manufacturing company.
One way we could use ANOVA, would be to try and compare several monthsâ€™ worth of data collected on electricity usage from the previous months. As per https://explorable.com/anova â€œWhen we have only two samples we can use the t-test to compare the means of the samples, but it might become unreliable in case of more than two samples.â€ Since we need reliability to make our case against the electric company, ANOVA seems like a good alternative.
This could tell us if our manufacturing facility has increased its usage of electricity in a statistically significant manner. We could formulate our null hypothesis, by establishing that Ho = Ma = Mb = Mc where Ma represents April, Mb represents May and Mc represents June. Then we could represent our Alternate hypothesis as saying that Ho is not true or that at least one of our samples differs from the others. In case this was true, it could indicate that our managers where wrong to assume that our process had not change and that someone or something, was using more power than in the previous months.
Now if we fail to reject our null hypothesis, then this might signal, that in fact we have not changed our power consumption requirements, and that the extra usage of electricity may be due to some other factors. Giving us a chance to refute the electric companyâ€™s claim.