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When should you use confidence interval and when should you use prediction interval?
Huangshawn253
Specifically, which to use in this scenario?
Recommend a wireless phone plan from the alternatives listed above. The company is going to commit to using this phone plan for a long time. [Hint: use the relevant Margin of Error here.]
It's from a test question. We're a given a regression analysis on phone minutes usage for a sample of company but the company stated in this question was not included in the sample (Therefore, prediction interval should be used?)
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∙ 12y ago
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Why do you use confidence intervals?
Statistical estimates cannot be exact: there is a degree of uncertainty associated with any statistical estimate. A confidence interval is a range such that the estimated value belongs to the confidence interval with the stated probability.
When you use a confidence interval to reach a conclusion about the population mean you are applying a type of reasoning or logic called?
normal distribution
When finding a confidence interval for true mean spent of all citizens should you us a z-score or t-score?
If the sample size is less then 30 use the T table, if greater then 30 use the Z table.
What is the relationship between confidence interval and standard deviation?
Short answer, complex. I presume you're in a basic stats class so your dealing with something like a normal distribution however (or something else very standard). You can think of it this way... A confidence interval re-scales margin of likely error into a range. This allows you to say something along the lines, "I can say with 95% confidence that the mean/variance/whatever lies within whatever and whatever" because you're taking into account the likely error in your prediction (as long as the distribution is what you think it is and all stats are what you think they are). This is because, if you know all of the things I listed with absolute certainty, you are able to accurately predict how erroneous your prediction will be. It's because central limit theory allow you to assume statistically relevance of the sample, even given an infinite population of data. The main idea of a confidence interval is to create and interval which is likely to include a population parameter within that interval. Sample data is the source of the confidence interval. You will use your best point estimate which may be the sample mean or the sample proportion, depending on what the problems asks for. Then, you add or subtract the margin of error to get the actual interval. To compute the margin of error, you will always use or calculate a standard deviation. An example is the confidence interval for the mean. The best point estimate for the population mean is the sample mean according to the central limit theorem. So you add and subtract the margin of error from that. Now the margin of error in the case of confidence intervals for the mean is za/2 x Sigma/ Square root of n where a is 1- confidence level. For example, confidence level is 95%, a=1-.95=.05 and a/2 is .025. So we use the z score the corresponds to .025 in each tail of the standard normal distribution. This will be. z=1.96. So if Sigma is the population standard deviation, than Sigma/square root of n is called the standard error of the mean. It is the standard deviation of the sampling distribution of all the means for every possible sample of size n take from your population ( Central limit theorem again). So our confidence interval is the sample mean + or - 1.96 ( Population Standard deviation/ square root of sample size. If we don't know the population standard deviation, we use the sample one but then we must use a t distribution instead of a z one. So we replace the z score with an appropriate t score. In the case of confidence interval for a proportion, we compute and use the standard deviation of the distribution of all the proportions. Once again, the central limit theorem tells us to do this. I will post a link for that theorem. It is the key to really understanding what is going on here!
What should a prediction start with?
A prediction should start with an analysis of past trends and data, followed by identifying patterns or relationships that can help inform the prediction. It's important to consider various factors that could influence the outcome and use appropriate methods or models to make an accurate prediction.
Why you use always 95 percent confidence interval?
You don't. There are times when you use 99% or 99.5%, and others where you will settle for 90%. The percentage chosen will depend on the implications of making the wrong decision.
When calculating the mean the researcher should use data measured at which level?
interval
Will The finite population correction factor lead to a wider confidence interval?
No since it is used to reduce the variance of an estimate in the case that the population is finite and we use a simple random sample.
For interval ratio data the correct measure of central tendency is?
Interval-Ratio can use all three measures, but the most appropriate should be mean unless there is high skew, then median should be used.
Should you use a friend to ask a girl out for you?
No, girls will think you have no self confidence and it will be a NO.
When to use should would and could?
we use SHOULD if we are expressing a necessity, obligation and prediction COULD if we are expressing a possibility or past ability WOULD if we are expressing a habitual action
When to use use and could?
we use SHOULD if we are expressing a necessity, obligation and prediction COULD if we are expressing a possibility or past ability WOULD if we are expressing a habitual action
