Iowa GED Statistical Report, 2002

The following information summarizes the major statistical trends relative to Iowa’s GED testing program for calendar year 2002

Iowa GED Statistical Report, 2006

The following information summarizes the major statistical trends relative to Iowa’s GED testing program for calendar Year 2005.

A-1 Monthly Public Assistance Statistical Report Family Investment Program, December 2006

A-1 Monthly Public Assistance Statistical Report Family Investment Program.

A-1 Monthly Public Assistance Statistical Report Family Investment Program, January 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program for January 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, February 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program - February 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, March 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - March 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, April 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - April 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, May 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - May 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, June 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - June 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, July 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - July 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, August 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - August 2007

Asymptotic robustness in multi-sample analysis of multivariate linear relations

Standard methods for the analysis of linear latent variable models oftenrely on the assumption that the vector of observed variables is normallydistributed. This normality assumption (NA) plays a crucial role inassessingoptimality of estimates, in computing standard errors, and in designinganasymptotic chi-square goodness-of-fit test. The asymptotic validity of NAinferences when the data deviates from normality has been calledasymptoticrobustness. In the present paper we extend previous work on asymptoticrobustnessto a general context of multi-sample analysis of linear latent variablemodels,with a latent component of the model allowed to be fixed across(hypothetical)sample replications, and with the asymptotic covariance matrix of thesamplemoments not necessarily finite. We will show that, under certainconditions,the matrix $\Gamma$ of asymptotic variances of the analyzed samplemomentscan be substituted by a matrix $\Omega$ that is a function only of thecross-product moments of the observed variables. The main advantage of thisis thatinferences based on $\Omega$ are readily available in standard softwareforcovariance structure analysis, and do not require to compute samplefourth-order moments. An illustration with simulated data in the context ofregressionwith errors in variables will be presented.

Iowa Sales and Use Tax Annual Statistical Report 1998

Iowa Sales and Use Tax Annual Statistical Report 1998

Iowa Sales and Use Tax Annual Statistical Report 1999

Iowa Sales and Use Tax Annual Statistical Report 1999

Iowa Sales and Use Tax Annual Statistical Report 2000

Iowa Sales and Use Tax Annual Statistical Report 2000