Confidence Interval Calculation

Is the probability of having a value less than or equal to x%. So for 99%, we subtract the level of significance (0.01) from 0.5 to get 0.45 and then find the corresponding value on the standard normal distribution table which is 1.65. This value is then used to calculate the confidence interval. However, for a 99% confidence interval, the calculator provides a value of 5. This may be because the calculator is using a different formula or method to calculate the confidence interval.
  • #1
MMCS
151
0
See attached for problem

Working:

Mean: 1203.26
Standard Deviation: 7.047

for 99% confidence interval, level of significance = 0.01
Therefore

0.5 - 0.01/2 = 0.45
Reading 0.45 from standard distribution table i get a value of 1.65

therefore confidence interval should be

1203.26 +/- (1.65*7.047)/√15

1203.26 +/- 3

however using this website to check my answer:

http://www.mccallum-layton.co.uk/tools/statistic-calculators/confidence-interval-for-mean-calculator/

It gives me a value of 5 (approx)

Thanks
 

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  • #2
MMCS said:
See attached for problem

Working:

Mean: 1203.26
Standard Deviation: 7.047

for 99% confidence interval, level of significance = 0.01
Therefore

0.5 - 0.01/2 = 0.45
Reading 0.45 from standard distribution table i get a value of 1.65

therefore confidence interval should be

1203.26 +/- (1.65*7.047)/√15

1203.26 +/- 3

however using this website to check my answer:

http://www.mccallum-layton.co.uk/tools/statistic-calculators/confidence-interval-for-mean-calculator/

It gives me a value of 5 (approx)

Thanks

For a (symmetric) 99% confidence on a quantity X, you want a 0.5% chance that X is below below the lower limit and a 0.5% chance that X is above the upper limit. In other words, if U is the upper limit you want Pr{ X ≤ U} = 1-0.005 = 0.995. What is the 99.5 percentile on the standard normal distribution?
 
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  • #3
Ray Vickson said:
For a (symmetric) 99% confidence on a quantity X, you want a 0.5% chance that X is below below the lower limit and a 0.5% chance that X is above the upper limit. In other words, if U is the upper limit you want Pr{ X ≤ U} = 1-0.005 = 0.995. What is the 99.5 percentile on the standard normal distribution?

Thanks for your reply, I'm not sure where the 0.005 value is from?
 
  • #4
MMCS said:
Thanks for your reply, I'm not sure where the 0.005 value is from?

What is meant by x%?
 
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Related to Confidence Interval Calculation

What is a confidence interval?

A confidence interval is a range of values that is likely to contain the true population parameter with a certain level of confidence. It is used to estimate the true value of a population parameter based on a sample of data.

How is the confidence interval calculated?

The confidence interval is calculated using the sample data and a specific formula that takes into account the sample size, standard deviation, and the desired level of confidence. The most commonly used formula is the margin of error formula: CI = x̅ ± z * (s/√n), where x̅ is the sample mean, s is the sample standard deviation, n is the sample size, and z is the critical value based on the desired level of confidence.

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

The confidence level represents the probability that the true population parameter falls within the calculated confidence interval. For example, a 95% confidence level means that if the same population was sampled 100 times, the calculated confidence interval would contain the true population parameter in 95 of those samples.

What are the factors that affect the width of a confidence interval?

The width of a confidence interval is affected by three main factors: the sample size, the standard deviation of the sample, and the chosen confidence level. A larger sample size and a smaller standard deviation will result in a narrower confidence interval, while a higher confidence level will result in a wider interval.

What are some common uses of confidence intervals?

Confidence intervals are commonly used in statistical analysis and research to estimate the true value of population parameters such as means, proportions, and differences between means. They are also used to assess the accuracy and precision of sample data and to compare different groups or treatments.

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