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Graduate students often come to statistics courses with varying levels of motivation and previous academic preparation. Within the statistics education literature, there is a growing consensus to guide instructors who want to help their students gain the requisite statistical knowledge so they can conduct their own research and report their results accurately. Recommendations from the literature include using real data, showing worked-out example problems, and providing immediate feedback to allow students to reflect on the correct and incorrect decisions they made in their analyses. This chapter describes the use of expert decision models (EDMs) in two graduate-level statistics courses – multiple regression and structural equation modeling. Decision-Based learning is an effective way to support graduate students’ developing thinking about statistics. In both courses, the students encounter the EDM through a series of assignments which guides students through the process of specifying a statistical model, running that model in Statistical Package for the Social Sciences or Mplus, and interpreting the results. These assignments use real datasets whenever possible and are designed to expose students to various issues they may experience in their research (missing data, violations of assumptions, etc.) and to illustrate how an expert would have adapted to those issues to complete the analysis. The EDM, with its just-in-time, just-enough instruction, helps students navigate these obstacles through guided practice and allows them to develop the conditional knowledge to handle issues that will arise as they carry out their own research.

The purpose of the work is to model disproportions in development of regional economy of Russia and to determine perspectives and recommendations for overcoming them and achieving the balance of the economy. The applied methods are based on Popkova's methodology of calculation of “underdevelopment whirlpools,” which allows conducting dynamic modeling of disproportions in development of regional economy. The research is performed in three consecutive stages. At the first stage, the dynamic model of development of the Russia's regional economy is compiled with the help of the methodology of “underdevelopment whirlpools” in federal districts of the Russian Federation based on GDP per capita. At the second stage, the key factors of emergence of disproportions in development of the Russia's regional economy are determined and models of multiple regression of development of the Russia's regional economy are compiled. At the third stage, target parameters of the determined factors are set for reducing the “underdevelopment whirlpools” in the Russia's regional economy by automatized solution of the optimization task with application of the simplex method and recommendations for overcoming the disproportions in development of the Russia's regional economy are compiled. As a result, it is concluded that regional economy of Russia is not well-balanced, as it has deep structural disproportions. These disproportions are caused by insufficient attention to peculiarities of regional economic systems during development and implementation of regional strategies of state management of economy. For more precise accounting of the influence of the key factors of appearance of disproportions and highly-effective management of them for overcoming the “underdevelopment whirlpools,” the algorithm of overcoming the disproportions in development of the Russia's regional economy is developed by the authors, which envisages various managerial measures depending on peculiarities of each Russian region.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

This chapter investigates the cost of public debt for firms using a comprehensive sample consisting of 17,368 industrial bond issues from 1970 to 2011. The empirical evidence shows that yield spreads for seasoned bond issues are significantly lower than those for initial bond issues. This seasoning effect is robust across different sample periods, subsamples, and model specifications. On average, the yield spreads for seasoned bond issues are around 50 bps lower than those for initial bond issues. This difference cannot be explained by other bond and firm characteristics. The seasoning effect is more pronounced for firms with higher levels of uncertainty, lower information disclosure quality, and longer time intervals between the first and subsequent issues. Our empirical findings provide supportive evidence for the extant theories that aim to rationalize the information role in determining the cost of capital.

This chapter introduces demographic variables in empirical regression to help find whether demographic changes have an impact on economic growth. There is evidence from estimated values in this chapter to suggest that there is no impact that demographic changes in Hong Kong is affecting the economic growth. The population growth has purely a transition impact where the fertility rate was low in early 2000 up to 2015 as the size of the dependency ratio increases. Besides testing demographic variables the government emphasises better education for all people of ages for prosperous growth but in fact has a negative response on educational investment on the growth of the economy. A well-educated country individual does not suggest a higher productivity in economy growth. An important implication is that there has been no single variable as yet that has seriously impacted the economy growth, but there will be changes in the coming years and has to be attended in result to avoid a diminishing economy.

Purpose – The purpose of this chapter is to examine methodological trends in emerging market strategy research and to provide a comprehensive review of methods of assessing group variation in comparative studies.

Methodology/approach – This comprises a systematic review of the methodology of emerging market research over the past 10 years, followed by methodological best practices for comparative studies involving emerging and mature markets, with exemplars from the past research.

Findings – Despite previous calls for more comparative studies in emerging market research, most of the literature is reporting on single-country studies. There is some confusion in terminology and the methods used in this area of strategy research. Increased attention to the “East” calls for a reevaluation of methods utilized in comparative studies. The methods described in this chapter present best practices for comparative research.

Social implications – More comparative studies would substantially expand our understanding of the differences between the emerging and developed markets, and the potential impact of emerging markets on global economy. Rigorous research methods extend validity and generalizability of the studies.

Originality/value – This chapter is the first study to date to analyze the methodological trends of the entire field of emerging market research over the span of 10 years and to provide systematic methodological recommendations tailored to analyzing variation in comparative studies.

This chapter elaborates on entrepreneurship in developed and developing countries and focuses on the optimization of entrepreneurial activities. Various scenarios are considered: independent functioning of the market, integration in the form of reorganization (mergers and acquisitions), integration in the form of clustering, and integration in the form of innovational networks and technological parks. The optimal structure of the integration processes and best-case scenarios for its implementation to accelerate the rate and increase the quality of economic growth are substantiated. The potential for uptake of integration processes in stimulating economic growth through entrepreneurship is determined by the level of institutionalization in an economy. In developed countries, all forms of company integration are characterized by the high level of institutionalization, which allows for their effective use for economic growth. Independent companies, mergers, and acquisitions restrain economic growth and reduce its quality, while clusters, technological parks, and innovational networks accelerate the rate of economic growth and increase its quality. In developing countries, integration processes in entrepreneurship have a different influence on economic growth and require further institutionalization.

Survival (default) data are frequently encountered in financial (especially credit risk), medical, educational, and other fields, where the “default” can be interpreted as the failure to fulfill debt payments of a specific company or the death of a patient in a medical study or the inability to pass some educational tests.

This paper introduces the basic ideas of Cox's original proportional model for the hazard rates and extends the model within a general framework of statistical data mining procedures. By employing regularization, basis expansion, boosting, bagging, Markov chain Monte Carlo (MCMC) and many other tools, we effectively calibrate a large and flexible class of proportional hazard models.

The proposed methods have important applications in the setting of credit risk. For example, the model for the default correlation through regularization can be used to price credit basket products, and the frailty factor models can explain the contagion effects in the defaults of multiple firms in the credit market.

Evaluating pain and discomfort in animals is difficult at best. Veterinarians believe however, that they can establish a proxy for estimating levels of pain and discomfort in canines by observing variations in their activity levels. Sufficient research has been conducted to justify this assertion, but little has been conducted to analyze the volumes of activity data collected. We present the first of a series of analyses aimed at ultimately presenting an effective predictive tool for canine pain and discomfort levels. In this chapter, we perform analyses on a dataset of normal (control) dogs, containing almost 3 million records. The forecasting analyses incorporated multiple polynomial regression models with transcendental transformations and ARIMA models to provide effective determination and prediction of baseline normal canine activity levels.