Can any one help me with confidence intervals I have no idea how to do them?

Suppose we wanted to calculate a 95% confidence interval for?. (Note: "97.5th" and "0.95" are correct in the preceding expressions. There is a 2.5% chance that T will be less than?

C and a 2.5% chance that it will be larger than +c. And we have a theoretical (stochastic) 95% confidence interval for?. An interval with fixed numbers as endpoints, of which we can no longer say there is a certain probability it contains the parameter?

; either? Is in this interval or isn't. Confidence intervals are one method of interval estimation, and the most widely used in frequentist statistics.

An analogous concept in Bayesian statistics is credible intervals, while an alternative frequentist method is that of prediction intervals which, rather than estimating parameters, estimate the outcome of future samples. For other approaches to expressing uncertainty using intervals, see interval estimation. There is disagreement about which of these methods produces the most useful results: the mathematics of the computations are rarely in question–confidence intervals being based on sampling distributions, credible intervals being based on Bayes' theorem–but the application of these methods, the utility and interpretation of the produced statistics, is debated.

Confidence intervals are an expression of probability and are subject to the normal laws of probability. If several statistics are presented with confidence intervals, each calculated separately on the assumption of independence, that assumption must be honoured or the calculations will be rendered invalid. For example, if a researcher generates a set of statistics with intervals and selects some of them as significant, the act of selecting invalidates the calculations used to generate the intervals.

A prediction interval for a random variable is defined similarly to a confidence interval for a statistical parameter. Consider an additional random variable Y which may or may not be statistically dependent on the random sample X. Here Pr?

,? Indicates the joint probability distribution of the random variables (X, Y), where this distribution depends on the statistical parameters (?,?).