stickerser.blogg.se - Statistical tools for data analysis r

As part of designing a neural network, we attempt to leverage a genetic algorithm to generate promising multilayer perceptron architectures for predicting credit ratings using Norwegian Corporate Accounts data. In this paper, we therefore investigate the extent to which deep learning can be used to predict corporate credit ratings.

Beyond simple cost reduction, such automation would also give suggestions for ratings from a purely objective perspective compared to the subjectivity of credit rating agencies.

Provides the SAS and R example code, datasets, and more onlineĬorporate credit rating is a complex and expensive process where automation may yield significant benefits.

Incorporates extensive examples of simulations.

Covers APIs, reproducible analysis, database management systems, MCMC methods, and finite mixture models.

Shows how RStudio can be used as a powerful, straightforward interface for R.

Includes an index for each software, allowing users to easily locate procedures.

Contains worked examples of basic and complex tasks, offering solutions to stumbling blocks often encountered by new users.

Takes users through the process of statistical coding from beginning to end.

Presents parallel examples in SAS and R to demonstrate how to use the software and derive identical answers regardless of software choice.

Numerous example analyses demonstrate the code in action and facilitate further exploration. SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Through the extensive indexing and cross-referencing, users can directly find and implement the material they need. It also includes extended examples of simulations and many new examples.

It incorporates a number of additional topics, including application program interfaces (APIs), database management systems, reproducible analysis tools, Markov chain Monte Carlo (MCMC) methods, and finite mixture models. This edition now covers RStudio, a powerful and easy-to-use interface for R. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and graphics, along with more complex applications. Retaining the same accessible format as the popular first edition, SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition explains how to easily perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation.