Coefficient of variance ?

[rclakmal]

coefficient of variance ?

ok i know that coefficient of variance is calculated using the equation
CV=(standard deviation /mean value)*100...

here is my problem ..Two TV manufacturing companies have provided the mean values and SD of the life time of their TVs .after calculating for one company i got CV as 18.7% and for other i got 16.9%.so i just want to know can i get a idea of what is the better company by looking@ this CV .if so how r u going approach ur answer ?i want reasons for that!...waiting for help ..thanks you!

(im giving mean and SD values here if u want them in ur argument but i think CV is sufficient ...for first mean =1495Hrs SD=280Hrs...for second mean=1875Hrs and SD=310Hrs)

 

[jbunniii]

Well, company 2's TVs last longer on average: 1875 hours versus 1495 for company 1. So if you buy a TV at random, the expected lifetime from company 2's TV is 380 hours longer.

The standard deviation (or coefficient of variance) simply indicates that there is enough variation that there is substantial overlap, meaning that if you get unlucky, you can end up with a TV from company 2 that dies sooner than a good one from company 1. But the odds are in company 2's favor.

Of course, none of this necessarily means that company 2 is "better." They could be GE, for all we know - saddled with toxic assets and requiring taxpayer bailouts! Or their TVs may last a long time but be ugly or have tinny sound. As Churchill said, "a vegetarian may well live for 100 years, but it will feel like 200."

 

[Architeuthis Dux]

Thank you for providing the necessary information. The coefficient of variation (CV) is a measure of variability or dispersion in a dataset. It is used to compare the variability of two or more datasets that have different units or scales. In this case, the CV values for the two TV manufacturing companies are 18.7% and 16.9%, respectively. Based on these values, it can be said that the second company has a lower variability in their TV's lifetime compared to the first company.

However, it is important to note that the CV alone cannot provide a complete picture of which company is better. Other factors such as the sample size, distribution of data, and the significance of the difference in CV values should also be taken into consideration.

For example, if the sample size for the first company is much larger than the second company, the CV value may be affected and may not accurately represent the true variability. Additionally, if the data is not normally distributed, the CV may not be a reliable measure of variability.

Therefore, it is important to conduct further analysis and consider other factors before making a conclusion about which company is better. I would suggest looking at other statistical measures such as confidence intervals and conducting hypothesis testing to determine the significance of the difference between the CV values. This will provide a more robust and evidence-based answer.