Confidence interval/statistic question

  • Thread starter abe_cooldude
  • Start date
In summary, the conversation discusses the goal of determining the durability of a bracket designed for 120lbs, with a range of 120-150lbs, based on weight and mileage data. The use of a Weibull plot and confidence interval is suggested to estimate the likelihood of success. The idea of analyzing durability through tests that are fail vs no-fail is also mentioned, but the specific jargon for this type of analysis is unknown.
  • #1
abe_cooldude
15
0
Hi all,

I am trying to do something that I am not sure how exactly it can be done. Not very good at statistics.

I have a bracket, sample size of 26, that was designed for 120lbs. It holds weight from 120lbs to 150lbs. Only 1 amount of weight gets applied at a time.

Each of the bracket gets tested for predefined number of miles, and each bracket in the sample size only gets tested once. If the bracket doesn't bracket and reaches the predefined miles, then it's good. Higher mileage means the bracket did good.

The goal is to see how long a bracket survives without breaking under a certain weight. Higher weight loading (150lbs vs. 120lbs) have higher weight in determining how good a bracket is and vice versa.

Here's what data looks like:
6 brackets at 120lbs on 12000/12000 miles course (good)
2 brackets at 120lbs on 10600/7600 miles course (better)
4 brackets at 120lbs on 12000/7600 miles course (even better)
4 brackets at 120lbs on 9100/7600 miles course (good)
4 brackets at 120lbs on 17700/7600 miles course (awesome)
2 brackets at 140lbs on 7600/7600 miles course (even better)
2 brackets at 150lbs on 12000/12000 miles course (greatest)
1 brackets at 150lbs on 3150/7600 miles course, broke (bad)
1 brackets at 120lbs on 4150/7600 miles course, broke (very bad)

I am trying to say that the bracket that was designed for 120lbs has a some percentage likely hood of doing well based on the data, and provide some kind of plot, confidence interval.

Any idea where to start or what I need to do? Not at all familiar with stats.

Thanks,
Abe
 
Physics news on Phys.org
  • #2
The more I read on the internet, the more I think this problem requires the use of Weibull plot.
 
  • #3
abe_cooldude said:
I am trying to say that the bracket that was designed for 120lbs has a some percentage likely hood of doing well based on the data, and provide some kind of plot, confidence interval.

As I understand the situation, both the weight supported and the mileage traveled are factors that tend to break a bracket. What probability do you want to estimate that involves both factors?

You could look for a table that says P percent of brackets will not break if they support a weight of W for M miles. That type of table would have 3 entries per line: P, W, M.

It's simpler to analyze the durability of a part from tests that are test-till-failure. However, I'm sure analyzing durability from tests that are fail vs no-fail has been studied and has its own jargon . Offhand, I don't know what that jargon is.
 

FAQ: Confidence interval/statistic question

1. What is a confidence interval?

A confidence interval is a range of values that is likely to include the true value of a population parameter with a certain level of confidence. It is often used in statistics to estimate the range of values within which the true population parameter lies.

2. How is a confidence interval calculated?

A confidence interval is calculated using a sample statistic and a margin of error. The sample statistic is used to estimate the true population parameter, and the margin of error takes into account the variability of the sample. The most common formula for calculating a confidence interval is: sample statistic ± (critical value x standard error of the statistic).

3. What is the significance of the confidence level in a confidence interval?

The confidence level in a confidence interval represents the probability that the true population parameter falls within the interval. For example, a confidence level of 95% means that there is a 95% chance that the true population parameter falls within the calculated interval.

4. How does sample size affect the width of a confidence interval?

Generally, larger sample sizes result in narrower confidence intervals. This is because a larger sample size reduces the margin of error and increases the precision of the estimate. However, the relationship between sample size and confidence interval width also depends on the variability of the sample data and the desired confidence level.

5. Can a confidence interval be used to make a conclusion about causation?

No, a confidence interval only provides information about the range of values in which the true population parameter is likely to fall. It does not determine causation or provide evidence of a cause-and-effect relationship between variables. Other statistical tests and analyses are needed to establish causation.

Similar threads

Back
Top