Critical Thinking : A Methodology for Interpreting Information 'deconstructs' common errors in thinking and teaches students to become smarter consumers of research results. Written to complement a textbook or a collection of readings, this brief methods book strengthens students' ability to interpret information whenever and wherever data are used. It includes a wide range of examples along with end of chapter exercises for further discussion. This book will be a coursebook for the undergraduate social science courses where critical thinking, numeracy, (...) and data literacy are common learning objectives"--Provided by publisher. (shrink)

The objective of this study was to jointly analyze the importance of cognitive and financial factors in the accuracy of profit forecasting by analysts. Data from publicly traded Brazilian companies in 2019 were obtained. We used text analysis to assess the cognitive biases from the qualitative reports of analysts. Further, we analyzed the data using statistical regression learning methods and statistical classification learning methods, such as Multiple Linear Regression, k-dependence Bayesian, and Random Forest. The Bayesian inference and classification methods allow (...) an expansion of the research line, especially in the area of machine learning, which can benefit from the examples of factors addressed in this research. The results indicated that, among cognitive biases, optimism had a negative relationship with forecasting accuracy while anchoring bias had a positive relationship. Commonality, to a lesser extent, also had a positive relationship with the analyst’s accuracy. Among financial factors, the most important aspects in the accuracy of analysts were volatility, indebtedness, and profitability. Age of the company, fair value, American Depositary Receipts, performance, and loss were still important but on a smaller scale. The results of the RF models showed a greater explanatory power. This research sheds light on the cognitive as well as financial aspects that influence the analyst’s accuracy, jointly using text analysis and machine learning methods, capable of improving the explanatory power of predictive models, together with the use of training models followed by testing. (shrink)