Top 10 Jobs in AI and the Right AI Skills [2025]
nlp pr :: Article Creator Example 5.5: Approximate Standard Errors Example 5.5: Approximate Standard Errors The NLP procedure provides a variety of ways for estimating parameters in nonlinear statistical models and for obtaining approximate standard errors and covariance matrices for the estimators. These methods are illustrated by estimating the mean of a random sample from a normal distribution with mean and standard deviation . The simplicity of the example makes it easy to compare the results of different methods in NLP with the usual estimator, the sample mean. The following data is used: data x; input x @@; datalines; 1 3 4 5 7 ; The standard error of the mean, computed with n-1 degrees of freedom, is 1. The usual maximum-likelihood approximation to the standard error of the mean, using a variance divisor of n rather than n-1, is 0.8944272. The sample mean is a least-squares estimator, so it can be computed using an...
