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Automounter is a standard Solaris feature that streamlines the management of shared file systems and reduces network traffic. Automounter also helps avoid file system problems in environments with many servers and many more clients by timing out on file system mounts instead of freezing one system s resources when a server from which it has mounted a file system becomes unavailable. In addition to the material presented here, we suggest that you refer to anything written by Hal Stern (see Bibliography and Recommended Reading) regarding NFS and automounter to get a more thorough picture than we can present here. Automounter uses centrally managed configuration information to specify the file systems and mount points for a network. By doing so, it helps to enforce a consistency in the overall naming and arrangement of file systems. It reduces the overhead of NFS mounts by dismounting file systems after a period of nonuse (the default is 5 minutes). Automounter is one of the busy sysadmin s best friends it provides what a user needs, when a user needs it, and then it goes away by itself. It dramatically reduces

Regression analysis uses the same principles for signi cance testing as analysis of variance (ANOVA) In regression the sum of squares (SS) is determined from the sum of the squared differences between an observed dependent value and its corresponding predicted value on the regression line at the same independent value It is helpful to imagine the regression line pivoting through the mean of the dependent and independent values (Fig 12) When the line is horizontal, the SS of the observed values is equal to the total SS because all the predicted values are the same as the mean of the dependent value As the line pivots, the SS reduces until the line of best t is reached The difference (or deviation) of an observed value from the predicted value in the regression is called the residual.

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It follows that (2.10) contains a Prohorov neighborhood. In order to show the converse, let E > 0 be given. Choose 6 < + E . In view of Lemma 2.5, there is a finite union of sets Ai with diameter < 6 such that

The top-level requirements towards a versioning model for knowledge management in the contexts targeted by the On-To-Knowledge project are enumerated below: 1. To allow tracking of changes in both ontologies and instance data with ne granularity. 2. To allow work with, revert to, extraction of, and branching of different states and versions of a knowledge base. This to be possible without any loss or corruption of information related to other states or versions.

From the residuals we calculate an error or residual SS, so called because it is not explained by the regression The difference between the total SS and the error SS is the regression SS; that is, the amount explained by the regression Thus the SS terms are additive: Total SS = regression SS + error SS The regression SS and error SS are divided by their respective degrees of freedom to give the regression and error mean squares (MS) The signi cance of the regression is then measured by the relative sizes of the regression MS and the error MS (F = regression MS/error MS), just as with the F test in ANOVA An example is given below Signi cance testing also applies to the parameters of the regression model.

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Remember that a planar surface was used in 26 with the Split feature to split the leg off of an imported part. This was more effective than a sketch or a plane because the split was limited to the bounds of the planar surface, not infinite like the sketch or the plane. The planar surface does not knit itself into the rest of the surface bodies around it automatically; you have to use the Knit feature to do this.

In the linear example the overall signi cance is the same as the signi cance of the parameter m because we are usually looking for signi cant departures from the horizontal; that is, a value of zero for m However, it is possible that our null hypothesis requires tests of departures from gradients other than zero We will see later how signi cant parameter values can be used in a variety of ecological and evolutionary models The method of linear regression will be illustrated with a grassland ecosystem example A study by Hui and Jackson (2006) explored the relationship between climate variables and above- and below-ground biomass in different grassland ecosystems across the world The examples came from.

pivot command SELECT Category, South, NorthEast, MidWest, West FROM (Select Category, Region, Amount from RawData) sq PIVOT (Sum (Amount) FOR Region IN (South, NorthEast, MidWest, West) ) AS pt

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