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A database is both a physical and logical grouping of data. An ESE database looks like a single file to Windows. Internally the database is a collection of 2, 4, 8, 16, or 32 KB pages (16 and 32 KB page options are only available in Windows 7 and Exchange 2010), [1] arranged in a balanced B-tree structure. [2]
Linear errors-in-variables models were studied first, probably because linear models were so widely used and they are easier than non-linear ones. Unlike standard least squares regression (OLS), extending errors in variables regression (EiV) from the simple to the multivariable case is not straightforward, unless one treats all variables in the same way i.e. assume equal reliability.
OLE DB separates the data store from the application that needs access to it through a set of abstractions that include the datasource, session, command, and rowsets. This was done because different applications need access to different types and sources of data, and do not necessarily want to know how to access functionality with technology ...
Any non-linear differentiable function, (,), of two variables, and , can be expanded as + +. If we take the variance on both sides and use the formula [11] for the variance of a linear combination of variables (+) = + + (,), then we obtain | | + | | +, where is the standard deviation of the function , is the standard deviation of , is the standard deviation of and = is the ...
The response variable may be non-continuous ("limited" to lie on some subset of the real line). For binary (zero or one) variables, if analysis proceeds with least-squares linear regression, the model is called the linear probability model. Nonlinear models for binary dependent variables include the probit and logit model.
β is a p × 1 column vector of unobservable parameters (the response coefficients of the dependent variable to each of the p independent variables in x i) to be estimated; z i is a scalar and is the value of another independent variable that is observed at time i or for the i th study participant;
Since this is a biased estimate of the variance of the unobserved errors, the bias is removed by dividing the sum of the squared residuals by df = n − p − 1, instead of n, where df is the number of degrees of freedom (n minus the number of parameters (excluding the intercept) p being estimated - 1). This forms an unbiased estimate of the ...
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