The Cost of Freedom - Netflix
Tue 18 June 2019
The Cost of Freedom is more than just dollars and cents, start your weekends in the know with hosts Neil Cavuto, David Asman, Eric Bolling, Dagen McDowell and a team of financial and political experts.
Type: Talk Show
Runtime: 120 minutes
The Cost of Freedom - Degrees of freedom (statistics) - Netflix
In statistics, the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary. The number of independent ways by which a dynamic system can move, without violating any constraint imposed on it, is called number of degrees of freedom. In other words, the number of degrees of freedom can be defined as the minimum number of independent coordinates that can specify the position of the system completely. Estimates of statistical parameters can be based upon different amounts of information or data. The number of independent pieces of information that go into the estimate of a parameter are called the degrees of freedom. In general, the degrees of freedom of an estimate of a parameter are equal to the number of independent scores that go into the estimate minus the number of parameters used as intermediate steps in the estimation of the parameter itself (most of the time the sample variance has N − 1 degrees of freedom, since it is computed from N random scores minus the only 1 parameter estimated as intermediate step, which is the sample mean). Mathematically, degrees of freedom is the number of dimensions of the domain of a random vector, or essentially the number of “free” components (how many components need to be known before the vector is fully determined). The term is most often used in the context of linear models (linear regression, analysis of variance), where certain random vectors are constrained to lie in linear subspaces, and the number of degrees of freedom is the dimension of the subspace. The degrees of freedom are also commonly associated with the squared lengths (or “sum of squares” of the coordinates) of such vectors, and the parameters of chi-squared and other distributions that arise in associated statistical testing problems. While introductory textbooks may introduce degrees of freedom as distribution parameters or through hypothesis testing, it is the underlying geometry that defines degrees of freedom, and is critical to a proper understanding of the concept.
The Cost of Freedom - In structural equation models - Netflix
When the results of structural equation models (SEM) are presented, they generally include one or more indices of overall model fit, the most common of which is a χ^2 statistic. This forms the basis for other indices that are commonly reported. Although it is these other statistics that are most commonly interpreted, the degrees of freedom of the χ^2 are essential to understanding model fit as well as the nature of the model itself. Degrees of freedom in SEM are computed as a difference between the number of unique pieces of information that are used as input into the analysis, sometimes called knowns, and the number of parameters that are uniquely estimated, sometimes called unknowns. For example, in a one-factor confirmatory factor analysis with 4 items, there are 10 knowns (the six unique covariances among the four items and the four item variances) and 8 unknowns (4 factor loadings and 4 error variances) for 2 degrees of freedom. Degrees of freedom are important to the understanding of model fit if for no other reason than that, all else being equal, the fewer degrees of freedom, the better indices such as χ^2 will be. It has been shown that degrees of freedom can be used by readers of papers that contain SEMs to determine if the authors of those papers are in fact reporting the correct model fit statistics. In the organizational sciences, for example, nearly half of papers published in top journals report degrees of freedom that are inconsistent with the models described in those papers, leaving the reader to wonder which models were actually tested.
The Cost of Freedom - References - Netflix