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Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

Using Multivariate Statistics

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The detection of psychiatric illness by questionnaire

The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

Analyzing linguistic data: a practical introduction to statistics using R

Background We aimed to study the presence of SARS-CoV-2 in air surrounding infected healthcare workers (HCW) in their homes versus infected patients who were undergoing potential aerosol-generating medical procedures (AGMP). We also studied the effect of different face masks worn bij infected persons on spread of SARS-CoV-2 into the air. Methods We developed a high-volume air sampler method that uses a household vacuum cleaner with a surgical mask serving as a sample filter. SARS-CoV-2 RNA was harvested from these sample filters and analyzed on the presence of RNA by polymerase chain reaction. We acquired air samples in close aproximity of HCWs wearing different facemasks. Also, we obtained free air samples away from the infected HCWs and samples near intensive care unit (ICU) patients undergoing AGMP. Fog experiments were performed to visualize the airflow around our air sampler. Results Aerosols were visibly suctioned into the vacuum cleaner when there was no face mask, whereas wearing a face mask resulted in a delayed and reduced flow of aerosols into the vacuum cleaner. The face masks that were worn by the HCWs were positive in 54-83% of cases. The proportion of positive air samples was higher in household settings of recently infected HCWs (29/41; 70.7%) compared to ICU settings (4/17; 23.5%) (p Conclusion This high-volume air sampler method was able to detect SARS-CoV-2 RNA in air samples. Air samples in the household environment of recently infected HCWs more frequently contained SARS-CoV-2 in comparison to those obtained in patient rooms during potential AGMP.

/pdf/exposition-to-airborne-sars-cov-2-in-household-and-hospitals-1wrk6v4vsq.pdf

Exposition to airborne SARS-CoV-2 in household and hospitals settings as studied by a vacuum cleaner as a high-powered air sampler

A previous study clustered home occupants into archetypes with a questionnaire. This study uses qualitative methods to strengthen those previously-found archetypes with data pertaining to the parti...

Substantiation of home occupant archetypes with the use of generative techniques: analysis and results of focus groups

Indoor air quality (IAQ) plays an important role in human health and well-being as people spend most of their time indoors. Among building materials, carpets covering high surface areas and having dense fibres have the potential to impact perceived IAQ. To explore the impact of carpets on perceived IAQ, it was studied whether low-emitting wool carpets can ‘clean’ the air. To assess the sorption effect of emissions of hardboard (as a permanent source) on carpet, untrained subjects were asked to assess a combination of low-odour emitting carpet and hardboard in one sample container and only hardboard in another sample container of test chamber. The results showed a slight (although not statistically relevant) difference in favour of the combination, indicating a slight adsorption effect.

/pdf/impact-of-carpets-on-perceived-indoor-air-quality-3da9lrj8.pdf

Impact of carpets on perceived indoor air quality

Indoor air quality (IAQ) is an important aspect of maintaining human health and well-being, particularly since people spend most of their time indoors. Carpets, with their large surface area and dense fibre piles, have the potential to significantly impact IAQ by emitting and absorbing volatile organic compounds (VOC) from building materials and human activities. The cleaning effect of wool carpets regarding the sorption of odours from two sources of pollution: hardboard and sweaty underwear (as a proxy for bio-effluents), was investigated with an untrained panel of subjects assessing the odour intensity and the acceptability. Tests were performed in three different test environments, including a sniffing table, CLIMPAQs, and full-scale test chambers. The outcome showed that wool carpets can potentially clean the air of odours in small-scale environments, where the wool carpet covers the floor and walls of the test environment, and the odour sources are in contact with the wool carpet. However, the results were less conclusive in on scale scenarios where wool carpets only covered the floor. Overall, wool carpets have the potential to ad(b)sorb odorous emissions, but only when these emissions are near the wool carpet, and thus can have the opportunity to be ad(b)sorbed.

https://journals.sagepub.com/doi/pdf/10.1177/1420326X231186308

Do wool carpets ‘clean’ the air or not? A study on the sorption effects of wool carpets by sensory evaluation

the European Joule II Project European Data Base for Indoor Air Pollution Sources in Buildings. The aim of the project is to produce a tool which would be used by designers to take into account the actual pollution of the air from the building elements and ventilation and air conditioning system components, in addition to the traditional pollution from occupants, considering the sensory, chemical and toxicologyc aspects. This data base is the first which contains sensory information. In this moment the software is near the final version in what refers the structure of the data base. The effort is now concentrated in the calculation procedures which will exploit the information contained in the data base in a useful way for the film' user

Philomena M. Bluyssen

Papers

Exposition to airborne SARS-CoV-2 in household and hospitals settings as studied by a vacuum cleaner as a high-powered air sampler

Substantiation of home occupant archetypes with the use of generative techniques: analysis and results of focus groups

Impact of carpets on perceived indoor air quality

Do wool carpets ‘clean’ the air or not? A study on the sorption effects of wool carpets by sensory evaluation

European data base on indoor air pollution sources in buildings: current status of data base structure and software