Seminar paper from the year 2010 in the subject Didactics - Business economics, Economic Pedagogy, grade: 1,7, Johannes Gutenberg University Mainz (Fachbereich 03: Rechts- und Wirtschaftswissenschaften, Lehrstuhl für Wirtschaftspädagogik ), course: Seminar: Education in the USA, language: English, abstract: This seminar paper deals with the question whether the American system of privatized postsecondary education (often referred to as the 'Ivy League') might be an exemplary approach to an optimized educational system. To answer this question, two perspectives are taken into account. On the one hand the effectiveness of the system is considered by considering a market model of education. On the other hand the efficiency of private postsecondary education will be examined. Hence, chapter 2 will define the term ‘Ivy League’ and present a market model of education. In section 3, the focus shifts to whether the system follows the principles of meritocracy and equality by examining influencing factors of the admission process including the role of high school education. Finally a conclusive summary of the findings is provided.

Seminar paper from the year 2010 in the subject Business economics - Didactics, Economic Pedagogy, grade: 1,7, Johannes Gutenberg University Mainz (Fachbereich 03: Rechts- und Wirtschaftswissenschaften, Lehrstuhl für Wirtschaftspädagogik ), course: Seminar: Education in the USA, language: English, abstract: This seminar paper deals with the question whether the American system of privatized postsecondary education (often referred to as the 'Ivy League') might be an exemplary approach to an optimized educational system. To answer this question, two perspectives are taken into account. On the one hand the effectiveness of the system is considered by considering a market model of education. On the other hand the efficiency of private postsecondary education will be examined. Hence, chapter 2 will define the term 'Ivy League' and present a market model of education. In section 3, the focus shifts to whether the system follows the principles of meritocracy and equality by examining influencing factors of the admission process including the role of high school education. Finally a conclusive summary of the findings is provided.

Named one of Vulture’s Top 10 Best Books of 2020! Leftist firebrand Fredrik deBoer exposes the lie at the heart of our educational system and demands top-to-bottom reform. Everyone agrees that education is the key to creating a more just and equal world, and that our schools are broken and failing. Proposed reforms variously target incompetent teachers, corrupt union practices, or outdated curricula, but no one acknowledges a scientifically-proven fact that we all understand intuitively: Academic potential varies between individuals, and cannot be dramatically improved. In The Cult of Smart, educator and outspoken leftist Fredrik deBoer exposes this omission as the central flaw of our entire society, which has created and perpetuated an unjust class structure based on intellectual ability. Since cognitive talent varies from person to person, our education system can never create equal opportunity for all. Instead, it teaches our children that hierarchy and competition are natural, and that human value should be based on intelligence. These ideas are counter to everything that the left believes, but until they acknowledge the existence of individual cognitive differences, progressives remain complicit in keeping the status quo in place. This passionate, voice-driven manifesto demands that we embrace a new goal for education: equality of outcomes. We must create a world that has a place for everyone, not just the academically talented. But we’ll never achieve this dream until the Cult of Smart is destroyed.

Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.

"Myth, Reality, and Reform bridges these critiques by balancing the importance of the four key functions of higher education: academic leadership, professional development, technological training and development, and general higher education. The book suggests how to consolidate the strengths of higher education systems while fundamentally reforming their weaker features.

Now in its third edition, this bestseller offers new data, recommendations, and observations that explore the choices for success available to students in the academic middle.