Brian D Marx Knihy

Using the word as loosely as some South Africans do today strips it of its intentional and intimate terror. The diluted use of the word empties out the pain it was designed to cause. In Stratcom, we aimed to annihilate, not just insert positive stories to support De Klerk. We were trained to permanently neutralize ideas, people, or institutions for the government. Stratcom was far from the tame version some South Africans, including political party supporters, now discuss on Twitter. Those who do should reflect on how it systematically dismantled Winnie Mandela, utilizing unlimited state resources to achieve this. Paul Erasmus’s explosive account of his time as a security policeman during apartheid reveals the National Party’s ruthless intent to destroy Mandela and terrorize anti-apartheid activists. He exposes the corruption and power struggles within the South African Police, highlighting the fascist connections some officers had. Erasmus confronts his own actions and the atrocities he was part of, naming individuals involved. His extensive testimony before the Truth and Reconciliation Commission provides insight into Stratcom's operations, and this book further explores that testimony, delving deeper into the moral reckoning he seeks.

Now in its second edition, this textbook offers a comprehensive introduction to parametric, nonparametric, and semiparametric regression, bridging the gap between theory and application. Key models and methods are presented with a solid formal foundation, illustrated through numerous examples and case studies. Important definitions and statements are summarized in boxes, and the underlying data sets and code are accessible online. The selection of methods emphasizes the availability of user-friendly software. Topics covered include classical linear models, generalized linear models, categorical regression models, mixed models, nonparametric regression, structured additive regression, quantile regression, and distributional regression models. Two appendices provide essential matrix algebra, probability calculus, and statistical inference. This revised edition expands on regression models, incorporating the relationship between regression and machine learning, enhancing details on statistical inference in structured additive regression, and offering a reworked chapter on quantile and distributional regression models. Regularization approaches are discussed more thoroughly throughout. The book targets students, educators, and practitioners in social, economic, and life sciences, as well as those in statistics, mathematics, and computer science interested in statistical modeling and data analysis, and is written at an intermediate