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# Linear Regression Standard Error Of Coefficients

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Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the Click the button below to return to the English verison of the page. The standard method of constructing confidence intervals for linear regression coefficients relies on the normality assumption, which is justified if either: the errors in the regression are normally distributed (the so-called UV lamp to disinfect raw sushi fish slices What examples are there of funny connected waypoint names or airways that tell a story? click site

If this is the case, then the mean model is clearly a better choice than the regression model. An example of case (ii) would be a situation in which you wish to use a full set of seasonal indicator variables--e.g., you are using quarterly data, and you wish to If the model is not correct or there are unusual patterns in the data, then if the confidence interval for one period's forecast fails to cover the true value, it is This statistic measures the strength of the linear relation between Y and X on a relative scale of -1 to +1. http://stats.stackexchange.com/questions/44838/how-are-the-standard-errors-of-coefficients-calculated-in-a-regression