Ha and Phuong are best friends in high school. But they took different paths in life after college and both are successful. One day, Phuong found out Ha was having an affair with her husband. After the divorce, Phuong tried really hard to move on and she found a new love in her life, Hoa, but she did not know whether she should be in love with him or not because he was younger than her. With Ha, she had her own problem with her life where her husband's family was strict with her and her husband and her mother-in-law hated her. It led her to have an affair with Adam, Phuong's husband, who Ha truly loved. Two women with different stories, they both had struggles in their lives and this is their story.
A vivid, accessible portrait of contemporary Vietnam through texts and complementary photographs that dispute the stereotypic images we have of this dynamic and diverse country.
Americans have access to some of the best science education in the world, but too often black students are excluded from these opportunities. This essential book by leading voices in the field of education reform offers an inspiring vision of how America’s universities can guide a new generation of African Americans to success in science. Educators, research scientists, and college administrators have all called for a new commitment to diversity in the sciences, but most universities struggle to truly support black students in these fields. Historically black colleges and universities (HBCUs) are different, though. Marybeth Gasman, widely celebrated as an education-reform visionary, and Thai-Huy Nguyen show that many HBCUs have proven adept at helping their students achieve in the sciences. There is a lot we can learn from these exemplary schools. Gasman and Nguyen explore ten innovative schools that have increased the number of black students studying science and improved those students’ performance. Educators on these campuses have a keen sense of their students’ backgrounds and circumstances, familiarity that helps their science departments avoid the high rates of attrition that plague departments elsewhere. The most effective science programs at HBCUs emphasize teaching when considering whom to hire and promote, encourage students to collaborate rather than compete, and offer more opportunities for black students to find role models among both professors and peers. Making Black Scientists reveals the secrets to these institutions’ striking successes and shows how other colleges and universities can follow their lead. The result is a bold new agenda for institutions that want to better serve African American students.
Make data-driven, informed decisions and enhance your statistical expertise in Python by turning raw data into meaningful insights Purchase of the print or Kindle book includes a free PDF eBook Key Features Gain expertise in identifying and modeling patterns that generate success Explore the concepts with Python using important libraries such as stats models Learn how to build models on real-world data sets and find solutions to practical challenges Book DescriptionThe ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation. This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more. By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.What you will learn Explore the use of statistics to make decisions under uncertainty Answer questions about data using hypothesis tests Understand the difference between regression and classification models Build models with stats models in Python Analyze time series data and provide forecasts Discover Survival Analysis and the problems it can solve Who this book is forIf you are looking to get started with building statistical models for your data sets, this book is for you! Building Statistical Models in Python bridges the gap between statistical theory and practical application of Python. Since you’ll take a comprehensive journey through theory and application, no previous knowledge of statistics is required, but some experience with Python will be useful.
One day, if you figure out for yourself that you too are a city-hopping nomad, you may have your own mission to conquer the roads that lead you into the big, big world. The destinations themselves may not be as important as the journeys. Less thinking, less doubt, less fear, less worry about judgment. Just go, and there will be a way...
This paper describes a new database of major labor and product market reforms covering 26 advanced economies over the period 1970-2013. The focus is on large changes in product market regulation in seven individual network industries, employment protection legislation for regular and temporary workers, and the replacement rate and duration of unemployment benefits. The main advantage of this dataset is the precise identification of the nature and date of major reforms, which is valuable in many empirical applications. By contrast, the dataset does not attempt to measure and compare policy settings across countries, and as such is no substitute for other publicly available indicators produced, for example, by the ILO, the OECD or the World Bank. It should also be seen as work in progress, for researchers to build on and improve upon. Based on the dataset, major reforms appear to have been more frequent in product markets than in labor markets in the last decades, and were predominantly implemented during the 1990s and 2000s.
The paper uses firm-level data to assess the financial health of the Vietnamese non-financial corporate sector on the eve of pandemic. Our analysis finds that smaller domestic firms were particularly vulnerable even by regional comparison. A sensitivity analysis suggests that the COVID-19 shock will have a substantial impact on firms’ profitability, liquidity and even solvency, particularly in the hardest hit sectors that are dominated by SMEs and account for a sizeable employment share, but large firms are not immune to the crisis. Risks of default can propagate more broadly through upstream and downstream linkages to industries not directly impacted, with stresses potentially translating into an increase in corporate bankruptcies and bank fragility. Policy measures taken in the immediate aftermath of the crisis have helped alleviate liquidity pressures, but the nature of policy support may have to pivot to support the recovery.
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