Search results
Results From The WOW.Com Content Network
For standard least squares estimation methods, the design matrix X must have full column rank p; otherwise perfect multicollinearity exists in the predictor variables, meaning a linear relationship exists between two or more predictor variables. This can be caused by accidentally duplicating a variable in the data, using a linear transformation ...
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.
The mean and the standard deviation of a set of data are descriptive statistics usually reported together. In a certain sense, the standard deviation is a "natural" measure of statistical dispersion if the center of the data is measured about the mean. This is because the standard deviation from the mean is smaller than from any other point.
The convolution of a function with a Gaussian is also known as a Weierstrass transform. A Gaussian function is the wave function of the ground state of the quantum harmonic oscillator. The molecular orbitals used in computational chemistry can be linear combinations of Gaussian functions called Gaussian orbitals (see also basis set (chemistry)).
In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed. [1] Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range. For instance, when the variance of data in a set is large, the data is widely scattered.
There are many forms of non-parametric smoothing methods to help estimate the function (). An interesting approach is to also look at a non-parametric variance function, () = (=). A non-parametric variance function allows one to look at the mean function as it relates to the variance function and notice patterns in the data.
The complement of the standard normal cumulative distribution function, () = (), is often called the Q-function, especially in engineering texts. [13] [14] It gives the probability that the value of a standard normal random variable will exceed : (>).
Here is the autocovariance function of X t, is the standard deviation of the input noise process, and , is the Kronecker delta function. Because the last part of an individual equation is non-zero only if m = 0 , the set of equations can be solved by representing the equations for m > 0 in matrix form, thus getting the equation