Analysis of Covariance With Qualitative Data

In data with a group structure, incidental parameters are included to control for missing variables. Applications include longitudinal data and sibling data. In general, the joint maximum likelihood estimator of the structural parameters is not consistent as the number of groups increases, with a fixed number of observations per group. Instead a conditional likelihood function is maximized, conditional on sufficient statistics for the incidental parameters. In the logit case, a standard conditional logit program can be used. Another solution is a random effects model, in which the distribution of the incidental parameters may depend upon the exogenous variables.

Testing Performace of Random Access Memory Using Linear Models

Various discussions relating to computers comment on a reasonable extent of random access memory (RAM) increase as it is a known fact that the extention of this type of memory influences speed of computer machines. Disputes often arise as to whether half a gigabyte extension of RAM is large enough for the computers to be significantly sped up, given the complexity of present software applications. In this article, we test statistically whether such an increase speeds up computers significantly or not, using analysis of covariance as a suitable statistical tool.RAM memory, linear model, analysis of covariance, deviance

On the bilinear covariants associated to mass dimension one spinors

In this paper we approach the issue of Clifford algebra basis deformation, allowing for bilinear covariants associated to Elko spinors which satisfy the Fierz-Pauli-Kofink identities. We present a complete analysis of covariance, taking into account the involved dual structure associated to Elko. Moreover, the possible generalizations to the recently presented new dual structure are performed.Comment: 9 pages, 0 figure

INVESTIGATING POWER OF ANALYSIS OF COVARIANCE METHODS

Analysis of covariance is a well-utilized statistical methodology. The procedure involves a series of statistical tests to first construct a most significant analysis model to characterize the effect of the covariate on response. Pairwise comparisons among treatments are then based on the finalized model. For traditional Normal error assumptions, each step of the process is based on exact statistical tests. However, the series of statistical tests defines a conditional probability scheme with possible multiplicity issues. The question then becomes if the analysis of covariance methodology considered in entirety is able to maintain a nominal level of significance with good power. Several procedures have been proposed in the literature suggesting different sequences of tests and understandings of analysis of covariance. This simulation study is being conducted to compare among a number of these analysis strategies. The initial goal was to investigate power of detecting treatment differences using the various analysis of covariance strategies. But, before power can be considered, the ability of the methodology to maintain a nominal level of significance must be investigated