In order to represent this information graphically, in the form of the confidence bands around the regression line, one has to proceed carefully and account for the joint distribution of the estimators.
22.
In the case of a simple regression involving a single independent variable, results can be presented in the form of a plot showing the estimated regression line along with either point-wise or simultaneous confidence bands.
23.
Methods particularly useful in analysis of variance, and in constructing simultaneous confidence bands for regressions involving basis functions are the Scheff?s method and, if the researcher has in mind only pairwise comparisons, the Tukey method.
24.
That formula would then reduce to one with the usual t distribution, which is appropriate for predicting / estimating for a SINGLE value of the independent variable, NOT for constructing a confidence band for a range of values of the independent value.
25.
In mathematical terms, a pointwise confidence band \ hat { f } ( x ) \ pm w ( x ) with coverage probability 1 & minus; ? satisfies the following condition separately for each value of " x ":
26.
A " 95 % simultaneous confidence band " is a collection of confidence intervals for all values " x " in the domain of " f ( x ) " that is constructed to have simultaneous coverage probability 0.95.
27.
Confidence bands are closely related to confidence intervals, which represent the uncertainty in an estimate of a single numerical value . " As confidence intervals, by construction, only refer to a single point, they are narrower ( at this point ) than a confidence band which is supposed to hold simultaneously at many points ."
28.
Confidence bands are closely related to confidence intervals, which represent the uncertainty in an estimate of a single numerical value . " As confidence intervals, by construction, only refer to a single point, they are narrower ( at this point ) than a confidence band which is supposed to hold simultaneously at many points ."
29.
By running enough missions over the same time period of interest ( different possible histories from the same starting point ), RAMP can be used to generate statistically significant results that establish the likely distribution of the user-defined parameters of interest and thus objectively assess the system, with the confidence bands on the results dependent on the number of missions simulated.
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