Many papers have been written on the subject - total employment rate, and most of them stressed on excessive pressure emanating from economic recession, heavy competition, modern and skill biased technological changes as the main principal causes of demands for jobs. Unarguably, evaluating total employment rate remain issues of considerable importance for economists, statisticians, the media and policy makers. However, understanding how the economy works requires a shift from economic modelling to economic analysis. We will consider the use of association model as an alternative to reduced form methods. Against the background of total employment rate, this study considers and estimate the most accurate association model of the Categorical Data Analysis for the total employment rate - employed persons aged 15-64 as a share of the total population of the same age group in the EU15 from 2008-2017. The analysis of association (ANOAS) table is given in order to ascertain the percentage of the data which is covered by each model. We estimate the association model to find the model with the best fit and acceptable. In conclusion we find out that the Column Effects Association Model (C) has the best fit because it covers almost 90% of the data - giving the best fit among all.

In fisheries production, aquaculture sector is characterised as a "blue revolution" as it is the fastest developing food industry in the world. Fish farming and aquaculture products are constantly gaining ground than ever before on our daily dishes. Fishes are a great source of affordable protein which the human body needs in regular and specific quantities and also it serves as a major pharmaceutical ingredient such as fish oil soap, body cream and perfume. Moreover, fishes are now being used as raw materials for fillets, canning for eateries and fish feeds. Statisticians and Nutrition/Dietician experts predict that much of the vital protein food necessary to nourish an increasing global population of which pathetically, many are underfed even today will come from marine (saltwater) fisheries. With a total production of 52 million tons in 2017, aquaculture is seen by many as the only solution to replenish the vacuum created in fishes due to an increase in consumption and overfishing. Aquaculture is important because it offers an alternative to overburdening and depleting marine fishery stocks. Currently the depletion rate of European fish stocks is 88%. This study considers the use of a logarithmic linear model to analyse fisheries and aquaculture products in EU26. We consider using data from the European Market Observatory for fisheries and aquaculture, estimated on actual base year from 2006-2017. The analysis of association table (ANOAS) is given to ascertain the percentage of the data which is covered by each model. We investigate and estimate the association model with the best fit and in conclusion, find out that the Row-Column Effects Association Model (RC) of the multivariate model (M=8) has the best fit among all, covering almost 91% of the total data observed.

Statistics on population change and the structure of population are increasingly used to support policymaking and to provide the opportunity to monitor demographic behaviour within political, economic, social and cultural contexts. Specifically, this concerns demographic developments that focus on a likely reduction in the relative importance of the working age population and a corresponding increase in the number of older persons. These statistics may be used to support a range of different analyses, including studies relating to population ageing and its effects on the sustainability of public finance and welfare, the evaluation of fertility as a background for family policies, or the economic and social impact of demographic change. This research aims to highlight the population change in twenty-five countries of the European Union. We consider the use of categorical data analysis to estimate the population change in EU25: absolute numbers and crude rates from 2003-2017. The data used in this study are from the Eurostat/World population prospects and estimated on actual base year from. Since the main focus is to have a better understanding of the population change in EU25, the analysis of association table (ANOAS) is given in order to ascertain the percentage of the data which is covered by each model. We find and estimate the association model with the best fit and in conclusion we find out that the Row-Column Effects Association Model (RC) of the multivariate model (M=4) has the best fit among all - covering a total of 99.9% of the data observed.

The structure of the European Union producer price indices, nominal total agricultural production varies from one country to another. The EU agricultural price indices involve the index of producer prices of agricultural products and the index of purchase prices of the means of agricultural production. The purpose of agricultural price indices is to unveil trends in the prices of individual agricultural products and purchase prices of the means of agricultural production. Moreover, the objective of the applying statistics on agricultural prices is to make comparisons between member states and also for economic analyses. Absolute agricultural prices are needed for many model calculations and for the ascertainment of price elasticity. The means through which these objectives could be achieved are believed to be when the absolute prices are compared between the member states, and also, when the products for which the prices from the respective member state are to be recorded for economic relevance. These objectives are not always compatible and sometimes require some compromise. In this study, we evaluate the price indices of domestic agricultural production as a whole in the EU24, using the most accurate association model of the Categorical Data Analysis. Figures from the Eurostat office calculated on annual base year from 2005-2017 were used to analyse this study. Since the main focus is to have a better understanding of producer price indices, nominal total domestic agricultural production, the analysis of association table (ANOAS) is given in order to ascertain the percentage of the data which is covered by each model. We find and estimate the association model with the best fit and in conclusion we find out that the Row-Effects Association Model (R) has the best fit because it covers 93% of the data, thereby giving the best fit among all.