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There's nothing random about it this is the book on statistical analysis with Excel Stunned by statistics? Exhausted by Excel? Relax! Statistical Analysis with Excel For Dummies, 4th Edition shows you how to use the world's most popular spreadsheet program to crunch numbers and interpret statistics even if you've never taken a statistics or advanced math course. In no time, you'll learn to use Excel to create and translate statistics in everyday life, understand common statistical terms, and improve your classroom or professional skills. Statistics has a reputation for being a challenging, math-intensive pursuit but it doesn't have to make your palms sweat. Using a minimum of equations and assuming no prior knowledge of statistics or Excel, this hands-on guide cuts through the jargon and shows you how to make sense of formulas and functions, charts and PivotTables, samples and normal distributions, probabilities and related distributions, trends and correlations, and much more. * Use Excel's tools to analyze and understand data * Apply statistical analysis to predict trends and make decisions * Interpret sales figures, gambling odds, and sports stats * Develop a grading curve or medical correlations Forget the mumbo jumbo! This guide shows you that statistical analysis with Excel can be easy, fun, and useful!

Regional innovation is a complex economic category and in the era of knowledge-based economy it determines regional development. The ambiguity of its measurement, which is the consequence of problems with the quantification of the test area as well as the difficulties arising from the lack or limited availability of certain empirical data, is a methodological challenge that makes scientific and public services of statistics play a huge role in the diagnosis of the problem. This issue has been developed and perfected for decades and recently research in this area has gained more importance. What proves to be extremely useful here are quantitative methods, in particular the methods of multivariate statistical analysis (SAW). In this article, by using the capabilities of public information databases of the CSO (GUS), a set of regional innovation indicators in Poland has been proposed – the set methodically convergent with EU guidelines. The region is associated with each of the sixteen existing voivodships. By using selected SAW methods changes in this area in recent years in Poland has been analyzed, including the assessment of the similarity of results over time.

Panel count data occur in studies that concern recurrent events, or event history studies, when study subjects are observed only at discrete time points. By recurrent events, we mean the event that can occur or happen multiple times or repeatedly. Examples of recurrent events include disease infections, hospitalizations in medical studies, warranty claims of automobiles or system break-downs in reliability studies. In fact, many other fields yield event history data too such as demographic studies, economic studies and social sciences. For the cases where the study subjects are observed continuously, the resulting data are usually referred to as recurrent event data. This book collects and unifies statistical models and methods that have been developed for analyzing panel count data. It provides the first comprehensive coverage of the topic. The main focus is on methodology, but for the benefit of the reader, the applications of the methods to real data are also discussed along with numerical calculations. There exists a great deal of literature on the analysis of recurrent event data. This book fills the void in the literature on the analysis of panel count data. This book provides an up-to-date reference for scientists who are conducting research on the analysis of panel count data. It will also be instructional for those who need to analyze panel count data to answer substantive research questions. In addition, it can be used as a text for a graduate course in statistics or biostatistics that assumes a basic knowledge of probability and statistics

The paper studies the regional integration as the unique process which depends on the degree of cooperation and interchange among regions. The generalisation of existing approaches for regional integration has been classified by the criterions. The data of the main economic indicators have been analysed. The economic analysis proves the differences in production endowments, the asymmetry in fixed capital investment, the disproportional income, and foreign direct investment distribution in 2001 2005 in Ukrainian regions. Econometric modelling depicts the existence of the division for the industrial regions with high urbanisation and backward agrarian regions in the Ukraine, the industrial development disparities among regions; the insufficient infrastructure (telecommunications, roads, hotels, services and etc.), the low labour productivity in industrial sector, and insufficient regional trade.

The purpose of this article is to explore the historical differences in wages between different groups (women, minorities, older employees, and other distinct groups) and to analyse whether wage gaps are due to discrimination. Statistical data on wages, population, and education were accumulated from the US Department of Education, the US Department of Labor, and the US Census Bureau. Other reference materials were used to examine various theories for pay differences. Specifically, the gender wage gap is partially due to women's domestic responsibilities and occupational sex‐typing. Race differences were found to have a correlation to education attainment. However, the wage gaps cannot completely be explained by other factors, indicating that negative stereotypes and discrimination do exist. The existence of discrimination is further supported by the growing number of pay discrimination lawsuits. In 2002, over $52 million was paid out by employers found guilty of employment discrimination in cases filed by the US Equal Employment Opportunity Commission. This article will define pay discrimination and examine ways to evaluate whether differences in pay are attributable to discrimination. Historical inequalities between men and women's pay will be presented and evaluated in detail. Race differences between White, African‐American, Hispanic and Asian employees will be thoroughly addressed. Other less common forms of pay discrimination will also be covered. The analysis will reveal the non‐discriminatory as well as discriminatory reasons for differences in pay among individuals with similar aptitude and provide suggestions for improving the situation.