![]() ![]() The confidence level, for example, a 95% confidence level, relates to how reliable the estimation procedure is, not the degree of certainty that the computed confidence interval contains the true value of the parameter being studied. A confidence interval is an alternative way of expressing the statistical significance.In statistics, a confidence interval is a range of values that is determined through the use of observed data, calculated at a desired confidence level that may contain the true value of the parameter being studied. In very simple terms, the difference reflects the difference between the statistical and practical significance of the regression equation. However, your confidence interval may be sufficiently small to make effective inferences at x1. For example, you may have an R-squared of 98%, however, when you run the prediction interval around x1 you may still find that the bounds around the predicted value are too large to be of practical value. It is important in determining the practical usage of the equation. In essence, the prediction interval tells you the 95% bound around the prediction of the value. ![]() The farther away that you predict from the overall mean value the less precise your prediction will be. The prediction interval is also influenced by how far away your are predicting from the mean. Both confidence and prediction intervals are calculated based on the error you have in the regression equation (residuals Mean squared error). ![]() The prediction interval will be wider than your confidence interval because now you are predicting the value Y1 from x1. The confidence interval tells you the 95% confidence bound around your estimated Y1 at level x1. Minitab will give you a confidence interval and a prediction interval. ![]()
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