Statistics: Confidence Intervals

In summary, the problem is to find the margin of error for a given confidence interval (c), sample standard deviation (s), and number of objects (n). The formula for the margin of error is E= (zc s) / n^.5, and the corresponding zc value for c=0.65 is 0.93. This can be found on the Level of Confidence Chart, where the zc value for c=0.65 is not present. One way to determine the zc value is through numerical integration of the normal distribution's pdf, or by using the error function.
  • #1
Phrynichos
3
0
The problem in question is as follows:

Find themargin of error for the given values of c, s, and n. where c is the confidence interval, s is the sample standard deviation and n is the number of objects.

c=0.65 s= 1.5 n=50

the formula: E= (zc s) / n^.5

Level of Confidence Chart

c ......zc
.80......1.28
.90......1.645
.95......1.96
.99.....2.575


As you can see, the zc value for c=.65 is not present on the chart. I don't know how to proceed to solve the problem by finding the corresponding zc for when c=.65. if anyone could show me, that would be appreciated.
thankx
 
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  • #2
[tex]z_{c}=0.93[/tex] for [tex]c=0.65[/tex]. Easy to find on your table for the Standard Normal Distribution.
 
  • #3
Hey LearnFrench,

How do they determine the 0.93 statistic in the first place? Assuming I have no life, how could I derive a table on my own?

Thanks,
HF08
 
  • #4
Numerical integration of the normal distribution's pdf would probably be the easiest way. The more traditional but also more complex way would start with the error function.
 

1. What is a confidence interval in statistics?

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

2. How is confidence level related to confidence interval?

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

3. How is a confidence interval calculated?

A confidence interval is calculated using the sample mean, standard deviation, sample size, and a critical value from a t-distribution or z-distribution. The formula is: sample mean ± (critical value * standard error).

4. What is the difference between a one-sided and two-sided confidence interval?

A one-sided confidence interval only considers one tail of the distribution, either the upper or lower tail. This is used when the researcher is only interested in a specific direction of the parameter. A two-sided confidence interval takes into account both tails of the distribution and is used when the researcher is interested in a range of possible values for the parameter.

5. What factors can affect the width of a confidence interval?

The width of a confidence interval can be affected by the sample size, the variability of the data, and the chosen confidence level. A larger sample size and lower variability will result in a narrower confidence interval, while a higher confidence level will result in a wider interval.

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