Academic literature on the topic 'Informell Statistisk Inferens'

Bayesian Statistics for Beginners is an entry-level book on Bayesian statistics. It is like no other math book you’ve read. It is written for readers who do not have advanced degrees in mathematics and who may struggle with mathematical notation, yet need to understand the basics of Bayesian inference for scientific investigations. Intended as a “quick read,” the entire book is written as an informal, humorous conversation between the reader and writer—a natural way to present material for those new to Bayesian inference. The most impressive feature of the book is the sheer length of the journ

This chapter introduces the most important aspects of Bayesian statistical inference and techniques for performing such calculations in practice. It first reviews the basic steps in Bayesian inference in early sections of the chapter, and then illustrates them with several examples in sections that follow. Numerical techniques for solving complex problems are next discussed, and the final section provides a summary of pros and cons for classical and Bayesian method. It argues that most users of Bayesian estimation methods are likely to use a mix of Bayesian and frequentist tools. The reverse is also true—frequentist data analysts, even if they stay formally within the frequentist framework, are often influenced by “Bayesian thinking,” referring to “priors” and “posteriors.” The most advisable position is to know both paradigms well, in order to make informed judgments about which tools to apply in which situations.

This paper explores the potential of dynamic statistics software for supporting the early teaching and learning of statistical and probabilistic concepts integrated within the mathematics curriculum. It shares the experiences from a case study that implemented a data-driven approach to mathematics instruction using the dynamic data-visualization software InspireData©, an educational package specifically designed to meet the learning needs of students in the middle and high school grades (Grades 4-12). The authors report on how a group of fourteen (n=14) Grade 4 (about 9-year-old) students used the affordances provided by the dynamic learning environment to gather, analyze, and interpret data, and to draw data-based conclusions and inferences. Findings from the study support the view that mathematics instruction can promote the development of learners' statistical reasoning at an early age, through an informal, data-based approach. They also suggest that the use of dynamic statistics software has the potential to enhance statistics instruction by scaffolding and extending young students' stochastical and mathematical reasoning.

Cannabis use continues to be a public health concern and has become a major clinical and nonclinical research construct in numerous fields for numerous reasons. First, the slight alterations in the criteria for cannabis use disorder (CUD) diagnosis required validation and prevalence statistics. Second, the shift in social perceptions of cannabis use and its legalization by various states have led to significant increases in cannabis use, abuse, and individuals meeting the criteria for CUD. Additionally, understanding the psychological and neural mechanisms associated with CUD will allow for innovative and tailored intervention and treatment methods for those suffering from the condition. This chapter provides a brief overview of the central, peripheral, and autonomic processes associated with CUD. It then considers the current treatments for CUD that are informed by pharmacological and epidemiological information. Finally, the authors contrast CUD with cardiovascular dysfunction, providing inferences for CUD as a potential contributor to the leading cause of death: cardiovascular disease.