Tax Credits Contingent Liabilities Brief October 3, 2007

The Tax Credits Contingent Liabilities Report was created by the Tax Research and Program Analysis Section of the Iowa Department of Revenue (IDR) for the benefit of the Revenue Estimating Conference (REC). This report is part of the Tax Credits Tracking and Analysis Program. The goal of the program is to provide a repository for information concerning the awarding, usage, and effectiveness of tax credits. This report forecasts tax credit claims assuming that all available awarded credits are issued and then, along with forecasted credits, are subsequently claimed.

Tax Credits Contingent Liabilities Brief December 7, 2007

The Tax Credits Contingent Liabilities Report was created by the Tax Research and Program Analysis Section of the Iowa Department of Revenue (IDR) for the benefit of the Revenue Estimating Conference (REC). This report is part of the Tax Credits Tracking and Analysis Program. The goal of the program is to provide a repository for information concerning the awarding, usage, and effectiveness of tax credits. This report forecasts tax credit claims assuming that all available awarded credits are issued and then, along with forecasted credits, are subsequently claimed.

Tax Credits Contingent Liabilities Brief April 2, 2008

The Tax Credits Contingent Liabilities Report was created by the Tax Research and Program Analysis Section of the Iowa Department of Revenue (IDR) for the benefit of the Revenue Estimating Conference (REC). This report is part of the Tax Credits Tracking and Analysis Program. The goal of the program is to provide a repository for information concerning the awarding, usage, and effectiveness of tax credits. This report forecasts tax credit claims assuming that all available awarded credits are issued and then, along with forecasted credits, are subsequently claimed.

Tax Credits Contingent Liabilities Brief July 31, 2008

The Tax Credits Contingent Liabilities Report was created by the Tax Research and Program Analysis Section of the Iowa Department of Revenue (IDR) for the benefit of the Revenue Estimating Conference (REC). This report is part of the Tax Credits Tracking and Analysis Program. The goal of the program is to provide a repository for information concerning the awarding, usage, and effectiveness of tax credits. This report forecasts tax credit claims assuming that all available awarded credits are issued and then, along with forecasted credits, are subsequently claimed.

Tax Credits Contingent Liabilities Brief, October 3, 2008

The Tax Credits Contingent Liabilities Report was created by the Tax Research and Program Analysis Section of the Iowa Department of Revenue (IDR) for the benefit of the Revenue Estimating Conference (REC). This report is part of the Tax Credits Tracking and Analysis Program. The goal of the program is to provide a repository for information concerning the awarding, usage, and effectiveness of tax credits. This report forecasts tax credit claims assuming that all available awarded credits are issued and then, along with forecasted credits, are subsequently claimed.

Tax Credits Contingent Liabilities Brief December 8, 2008

The Tax Credits Contingent Liabilities Report was created by the Tax Research and Program Analysis Section of the Iowa Department of Revenue (IDR) for the benefit of the Revenue Estimating Conference (REC). This report is part of the Tax Credits Tracking and Analysis Program. The goal of the program is to provide a repository for information concerning the awarding, usage, and effectiveness of tax credits. This report forecasts tax credit claims assuming that all available awarded credits are issued and then, along with forecasted credits, are subsequently claimed.

Tax Credits Contingent Liabilities Brief March 17, 2009

The Tax Credits Contingent Liabilities Report was created by the Tax Research and Program Analysis Section of the Iowa Department of Revenue (IDR) for the benefit of the Revenue Estimating Conference (REC). This report is part of the Tax Credits Tracking and Analysis Program. The goal of the program is to provide a repository for information concerning the awarding, usage, and effectiveness of tax credits. This report forecasts tax credit claims assuming that all available awarded credits are issued and then, along with forecasted credits, are subsequently claimed.

Tax Credits Contingent Liabilities Brief August 14, 2009

The Tax Credits Contingent Liabilities Report was created by the Tax Research and Program Analysis Section of the Iowa Department of Revenue (IDR) for the benefit of the Revenue Estimating Conference (REC). This report is part of the Tax Credits Tracking and Analysis Program. The goal of the program is to provide a repository for information concerning the awarding, usage, and effectiveness of tax credits. This report forecasts tax credit claims assuming that all available awarded credits are issued and then, along with forecasted credits, are subsequently claimed.

Tax Credits Contingent Liabilities Brief October 7, 2009

The Tax Credits Contingent Liabilities Report was created by the Tax Research and Program Analysis Section of the Iowa Department of Revenue (IDR) for the benefit of the Revenue Estimating Conference (REC). This report is part of the Tax Credits Tracking and Analysis Program. The goal of the program is to provide a repository for information concerning the awarding, usage, and effectiveness of tax credits. This report forecasts tax credit claims assuming that all available awarded credits are issued and then, along with forecasted credits, are subsequently claimed.

Tax Credits Contingent Liabilities Brief, December 11, 2009

The Tax Credits Contingent Liabilities Report was created by the Tax Research and Program Analysis Section of the Iowa Department of Revenue (IDR) for the benefit of the Revenue Estimating Conference (REC). This report is part of the Tax Credits Tracking and Analysis Program. The goal of the program is to provide a repository for information concerning the awarding, usage, and effectiveness of tax credits. This report forecasts tax credit claims assuming that all available awarded credits are issued and then, along with forecasted credits, are subsequently claimed.

Tax Credits Contingent Liabilities Brief March 11, 2010

The Tax Credits Contingent Liabilities Report was created by the Tax Research and Program Analysis Section of the Iowa Department of Revenue (IDR) for the benefit of the Revenue Estimating Conference (REC). This report is part of the Tax Credits Tracking and Analysis Program. The goal of the program is to provide a repository for information concerning the awarding, usage, and effectiveness of tax credits. This report forecasts tax credit claims assuming that all available awarded credits are issued and then, along with forecasted credits, are subsequently claimed.

Why Italy and not Spain?: comparing two industrialization processes from a dissagregate time-series perspective

This study presents new evidence concerning the uneven processes of industrialization innineteenth century Spain and Italy based on a disaggregate analysis of the productivesectors from which the behaviour of the aggregate indices is comprised. The use of multivariate time-series analysis techniques can aid our understanding and characterization of these two processes of industrialization. The identification of those sectors with key rolesin leading industrial growth provides new evidence concerning the factors that governed thebehaviour of the aggregates in the two economies. In addition, the analysis of the existenceof interindustry linkages reveals the scale of the industrialization process, and wheresignificant differences exist, accounts for many of the divergences recorded in the historiography for the period 1850-1913.

Why Italy and not Spain?: comparing two industrialization processes from a dissagregate time-series perspective

This study presents new evidence concerning the uneven processes of industrialization innineteenth century Spain and Italy based on a disaggregate analysis of the productivesectors from which the behaviour of the aggregate indices is comprised. The use of multivariate time-series analysis techniques can aid our understanding and characterization of these two processes of industrialization. The identification of those sectors with key rolesin leading industrial growth provides new evidence concerning the factors that governed thebehaviour of the aggregates in the two economies. In addition, the analysis of the existenceof interindustry linkages reveals the scale of the industrialization process, and wheresignificant differences exist, accounts for many of the divergences recorded in the historiography for the period 1850-1913.

Automated scoring of AFLPs using RawGeno v 2.0, a free R CRAN library.

Amplified Fragment Length Polymorphisms (AFLPs) are a cheap and efficient protocol for generating large sets of genetic markers. This technique has become increasingly used during the last decade in various fields of biology, including population genomics, phylogeography, and genome mapping. Here, we present RawGeno, an R library dedicated to the automated scoring of AFLPs (i.e., the coding of electropherogram signals into ready-to-use datasets). Our program includes a complete suite of tools for binning, editing, visualizing, and exporting results obtained from AFLP experiments. RawGeno can either be used with command lines and program analysis routines or through a user-friendly graphical user interface. We describe the whole RawGeno pipeline along with recommendations for (a) setting the analysis of electropherograms in combination with PeakScanner, a program freely distributed by Applied Biosystems; (b) performing quality checks; (c) defining bins and proceeding to scoring; (d) filtering nonoptimal bins; and (e) exporting results in different formats.

Adapting football to the child: an application of the logistic regression model in observational mewthodology.

Logistic regression is included into the analysis techniques which are valid for observationalmethodology. However, its presence at the heart of thismethodology, and more specifically in physical activity and sports studies, is scarce. With a view to highlighting the possibilities this technique offers within the scope of observational methodology applied to physical activity and sports, an application of the logistic regression model is presented. The model is applied in the context of an observational design which aims to determine, from the analysis of use of the playing area, which football discipline (7 a side football, 9 a side football or 11 a side football) is best adapted to the child"s possibilities. A multiple logistic regression model can provide an effective prognosis regarding the probability of a move being successful (reaching the opposing goal area) depending on the sector in which the move commenced and the football discipline which is being played.