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A reference guide for applications of SEM using Mplus Structural Equation Modeling: Applications Using Mplus is intended as both a teaching resource and a reference guide. Written in non-mathematical terms, this book focuses on the conceptual and practical aspects of Structural Equation Modeling (SEM). Basic concepts and examples of various SEM models are demonstrated along with recently developed advanced methods, such as mixture modeling and model-based power analysis and sample size estimate for SEM. The statistical modeling program, Mplus, is also featured and provides researchers with a flexible tool to analyze their data with an easy-to-use interface and graphical displays of data and analysis results. Key features: Presents a useful reference guide for applications of SEM whilst systematically demonstrating various advanced SEM models, such as multi-group and mixture models using Mplus. Discusses and demonstrates various SEM models using both cross-sectional and longitudinal data with both continuous and categorical outcomes. Provides step-by-step instructions of model specification and estimation, as well as detail interpretation of Mplus results. Explores different methods for sample size estimate and statistical power analysis for SEM. By following the examples provided in this book, readers will be able to build their own SEM models using Mplus. Teachers, graduate students, and researchers in social sciences and health studies will also benefit from this book.

Geometry and topology are both central concepts in geospatial data modelling and management. While geometry is the central concept for computational geometry applications, e.g. to intersect surfaces and solids in 3D space, topology is helpful for many application classes starting from city modelling to subsurface modelling and indoor navigation. Furthermore, p-adic numbers are useful for describing hierarchical processes on topological models. In this paper we first shortly review geometry- and topology-based approaches used for geospatial applications. We then describe our approach on turning geometry “upside down” focusing on topology during the whole process of distributed geospatial computing, data modelling, data management, and simulation. Furthermore, the way to use p-adic numbers for the description of hierarchical processes on topological models is shown for the example of simulation and the idea of p-adic analysis in distributed simulations is presented in the context of topological city and building models. The approach opens new insights such as topological relationships, components, and efficiently studying of the approximate behaviour of processes in the built environment using distributed computational systems. Finally, exemplary applications are presented to underline the importance of this new approach.

We constrain the intrinsic Eddington ratio (λ Edd) distribution function for local active galactic nuclei (AGN) in bins of low and high obscuration [log(NH/cm−2)≤22 and 22 −1), the trend is reversed, with <30% of AGN having log(NH/cm−2)>22 , which we suggest is mainly due to the small fraction of time spent in a highly obscured state. Considering the Eddington ratio distribution function of narrow-line and broad-line AGN from our prior work, we see a qualitatively similar picture. To disentangle temporal and geometric effects at high λ Edd, we explore plausible clearing scenarios such that the time-weighted covering factors agree with the observed population ratio. We find that the low fraction of obscured AGN at high λ Edd is primarily due to the fact that the covering factor drops very rapidly, with more than half the time spent with <10% covering factor. We also find that nearly all obscured AGN at high-λ Edd exhibit some broad lines. We suggest that this is because the height of the depleted torus falls below the height of the broad-line region, making the latter visible from all lines of sight.

Deploy your data ingestion pipeline, orchestrate, and monitor efficiently to prevent loss of data and quality Key Features Harness best practices to create a Python and PySpark data ingestion pipelineSeamlessly automate and orchestrate your data pipelines using Apache AirflowBuild a monitoring framework by integrating the concept of data observability into your pipelines Book Description Data Ingestion with Python Cookbook offers a practical approach to designing and implementing data ingestion pipelines. It presents real-world examples with the most widely recognized open source tools on the market to answer commonly asked questions and overcome challenges. You’ll be introduced to designing and working with or without data schemas, as well as creating monitored pipelines with Airflow and data observability principles, all while following industry best practices. The book also addresses challenges associated with reading different data sources and data formats. As you progress through the book, you’ll gain a broader understanding of error logging best practices, troubleshooting techniques, data orchestration, monitoring, and storing logs for further consultation. By the end of the book, you’ll have a fully automated set that enables you to start ingesting and monitoring your data pipeline effortlessly, facilitating seamless integration with subsequent stages of the ETL process. What you will learn Implement data observability using monitoring toolsAutomate your data ingestion pipelineRead analytical and partitioned data, whether schema or non-schema basedDebug and prevent data loss through efficient data monitoring and loggingEstablish data access policies using a data governance frameworkConstruct a data orchestration framework to improve data quality Who this book is for This book is for data engineers and data enthusiasts seeking a comprehensive understanding of the data ingestion process using popular tools in the open source community. For more advanced learners, this book takes on the theoretical pillars of data governance while providing practical examples of real-world scenarios commonly encountered by data engineers.

[This book] reviews the basic concepts and applications of SEM using Mplus Version 6. ... The first two chapters introduce the fundamental concepts of SEM and important basics of the Mplus program. The remaining chapters focus on SEM applications and include a variety of SEM models presented within the context of three sections: Single-group analyses, Multiple-group analyses, and other important topics, the latter of which includes the multitrait-multimethod, latent growth curve, and multilevel models. (DIPF/Orig.).

Revamp your data landscape and implement highly effective data pipelines in AWS with this hands-on guide Purchase of the print or Kindle book includes a free PDF eBook Key Features Execute extract, transform, and load (ETL) tasks on data lakes, data warehouses, and databasesImplement effective Pandas data operation with data wranglerIntegrate pipelines with AWS data services Book Description Data wrangling is the process of cleaning, transforming, and organizing raw, messy, or unstructured data into a structured format. It involves processes such as data cleaning, data integration, data transformation, and data enrichment to ensure that the data is accurate, consistent, and suitable for analysis. Data Wrangling on AWS equips you with the knowledge to reap the full potential of AWS data wrangling tools. First, you’ll be introduced to data wrangling on AWS and will be familiarized with data wrangling services available in AWS. You’ll understand how to work with AWS Glue DataBrew, AWS data wrangler, and AWS Sagemaker. Next, you’ll discover other AWS services like Amazon S3, Redshift, Athena, and Quicksight. Additionally, you’ll explore advanced topics such as performing Pandas data operation with AWS data wrangler, optimizing ML data with AWS SageMaker, building the data warehouse with Glue DataBrew, along with security and monitoring aspects. By the end of this book, you’ll be well-equipped to perform data wrangling using AWS services. What you will learn Explore how to write simple to complex transformations using AWS data wranglerUse abstracted functions to extract and load data from and into AWS datastoresConfigure AWS Glue DataBrew for data wranglingDevelop data pipelines using AWS data wranglerIntegrate AWS security features into Data Wrangler using identity and access management (IAM)Optimize your data with AWS SageMaker Who this book is for This book is for data engineers, data scientists, and business data analysts looking to explore the capabilities, tools, and services of data wrangling on AWS for their ETL tasks. Basic knowledge of Python, Pandas, and a familiarity with AWS tools such as AWS Glue, Amazon Athena is required to get the most out of this book.

This book demonstrates the power of Splunk 7.x to offer you quick solutions and strategies to bring efficient operational intelligence in your organization. Implement a wide range of tasks in recipe format to perform operations on machine data. Learn to achieve intelligent data-driven way using machine learning capabilities to explore complex data.

Learn how to architect, implement, and administer a complex Splunk Enterprise environment and extract valuable insights from business data. Key Features * Understand the various components of Splunk and how they work together to provide a powerful Big Data analytics solution. * Collect and index data from a wide variety of common machine data sources * Design searches, reports, and dashboard visualizations to provide business data insights Book Description Splunk is a leading platform and solution for collecting, searching, and extracting value from ever increasing amounts of big data - and big data is eating the world! This book covers all the crucial Splunk topics and gives you the information and examples to get the immediate job done. You will find enough insights to support further research and use Splunk to suit any business environment or situation. Splunk 7.x Quick Start Guide gives you a thorough understanding of how Splunk works. You will learn about all the critical tasks for architecting, implementing, administering, and utilizing Splunk Enterprise to collect, store, retrieve, format, analyze, and visualize machine data. You will find step-by-step examples based on real-world experience and practical use cases that are applicable to all Splunk environments. There is a careful balance between adequate coverage of all the critical topics with short but relevant deep-dives into the configuration options and steps to carry out the day-to-day tasks that matter. By the end of the book, you will be a confident and proficient Splunk architect and administrator. What you will learn * Design and implement a complex Splunk Enterprise solution * Configure your Splunk environment to get machine data in and indexed * Build searches to get and format data for analysis and visualization * Build reports, dashboards, and alerts to deliver critical insights * Create knowledge objects to enhance the value of your data * Install Splunk apps to provide focused views into key technologies * Monitor, troubleshoot, and manage your Splunk environment Who this book is for This book is intended for experienced IT personnel who are just getting started working with Splunk and want to quickly become proficient with its usage. Data analysts who need to leverage Splunk to extract critical business insights from application logs and other machine data sources will also benefit from this book.

Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.

The industrial processes that require the use of the web require a control system which allows for preserving the properties of the web unaltered, avoiding the risk of wrinkling, tearing, breakage and other defects. This control generally takes place by detecting the tension and the speed in certain points of the system since these variables determine the stress state on the web, which, if altered beyond certain ranges, can lead to the defects mentioned above. The problem of tension and web speed control is very demanding because the system’s dynamic is a function of many process variables that often vary over a wide range. In this study, an experimental system consisting of 12 rollers, four motorised, was analysed. This system was divided into four subsystems according to the logic of decentralised control. The tension of the initial and final subsystems and the speeds of the two central subsystems were monitored. This study proposes estimating continuous-time transfer functions using experimental time-domain data. A nonlinear least-squares search-based method minimises a weighted prediction error norm for directly identifying the mathematical model used to describe the web transport system. To test the performance of the proposed strategy, experimental data were collected in an open-loop configuration with constant voltage given to the four servo motors. The collected data were subsequently processed to define an extremely simple system model composed of a very limited number of parameters representing the system through transfer functions. The model was further validated by comparing the results obtained through simulations with the experimental data obtained with different inputs, and was also validated with some closed-loop tests.