Book•
Linear Regression Analysis: Theory and Computing
05 Jun 2009-
TL;DR: This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the methods and techniques described in the book.
Abstract: This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the methods and techniques described in the book. It covers the fundamental theories in linear regression analysis and is extremely useful for future research in this area. The examples of regression analysis using the Statistical Application System (SAS) are also included. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject fields. Introduction Simple Linear Regression Multiple Linear Regression Detection of Outliers and Influential Observations in Multiple Linear Regression Model Selection Model Diagnostics Extensions of Least Squares Generalized Linear Models Bayesian Linear Regression
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TL;DR: The effectiveness of using machine learning for model-free prediction of spatiotemporally chaotic systems of arbitrarily large spatial extent and attractor dimension purely from observations of the system's past evolution is demonstrated.
Abstract: We demonstrate the effectiveness of using machine learning for model-free prediction of spatiotemporally chaotic systems of arbitrarily large spatial extent and attractor dimension purely from observations of the system's past evolution. We present a parallel scheme with an example implementation based on the reservoir computing paradigm and demonstrate the scalability of our scheme using the Kuramoto-Sivashinsky equation as an example of a spatiotemporally chaotic system.
916 citations
Cites background from "Linear Regression Analysis: Theory ..."
...Since v 1⁄4 Wout1⁄2r;p is assumed to be linear in the parameters p, the problem of determining p, and hence Wout, is a simple linear regression [15]....
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TL;DR: This study validates the applicability of ML, voting, bagging, and stacking techniques for simple and efficient simulations of concrete compressive strength.
244 citations
Cites methods from "Linear Regression Analysis: Theory ..."
...The LR model applies four regression methods using ordinary least squares estimation: enter, stepwise, forward, and backward [49]....
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TL;DR: This work has performed linear regression, Multilayer perceptron and Vector autoregression method for desire on the CO VID-19 Kaggle data to anticipate the epidemiological example of the ailment and pace of COVID-2019 cases in India.
Abstract: Coronavirus disease (COVID-19) is an inflammation disease from a new virus. The disease causes respiratory ailment (like influenza) with manifestations, for example, cold, cough and fever, and in progressively serious cases, the problem in breathing. COVID-2019 has been perceived as a worldwide pandemic and a few examinations are being led utilizing different numerical models to anticipate the likely advancement of this pestilence. These numerical models dependent on different factors and investigations are dependent upon potential inclination. Here, we presented a model that could be useful to predict the spread of COVID-2019. We have performed linear regression, Multilayer perceptron and Vector autoregression method for desire on the COVID-19 Kaggle data to anticipate the epidemiological example of the ailment and pace of COVID-2019 cases in India. Anticipated the potential patterns of COVID-19 effects in India dependent on data gathered from Kaggle. With the common data about confirmed, death and recovered cases across India for over the time length helps in anticipating and estimating the not so distant future. For extra assessment or future perspective, case definition and data combination must be kept up persistently.
241 citations
TL;DR: In this article, the authors present and test two models that describe work engagement and its constituent dimensions (vigor, dedication, absorption) as mediating the relationship between organizational identification and job satisfaction.
Abstract: Purpose – Organizational identification refers to a person’s sense of belonging within the organization in which they work. Despite the importance of organizational identification for work-related attitudes and organizational behavior, little research has directly examined the mechanisms that may link these. The purpose of this paper is to provide an understanding of how organizational identification relates to job satisfaction. Design/methodology/approach – Adopting a social identity perspective, the authors present and test two models that describe work engagement and its constituent dimensions (vigor, dedication, absorption) as mediating the relationship between organizational identification and job satisfaction. Findings – Bootstrapped mediation analyses provided support for full mediation whereby there is an indirect (via work engagement) and positive effect of organizational identification on job satisfaction. Analyses also provided support for the mediating effects of the three dimensions of work e...
203 citations
Cites methods from "Linear Regression Analysis: Theory ..."
...Prior to the analyses, all variables used in the models were checked for multicollinearity (by examining the Variation Inflation Factors) and no issues were detected since all VIF values were 5 and not beyond the threshold of 10 (Yan & Su, 2009)....
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TL;DR: In this article, the trends and variability of PM 10, PM 2.5 and PM coarse concentrations at seven urban and rural background stations in five European countries for the period between 1998 and 2010 were investigated.
Abstract: . The trends and variability of PM 10 , PM 2.5 and PM coarse concentrations at seven urban and rural background stations in five European countries for the period between 1998 and 2010 were investigated. Collocated or nearby PM measurements and meteorological observations were used in order to construct Generalized Additive Models, which model the effect of each meteorological variable on PM concentrations. In agreement with previous findings, the most important meteorological variables affecting PM concentrations were wind speed, wind direction, boundary layer depth, precipitation, temperature and number of consecutive days with synoptic weather patterns that favor high PM concentrations. Temperature has a negative relationship to PM 2.5 concentrations for low temperatures and a positive relationship for high temperatures. The stationary point of this relationship varies between 5 and 15 °C depending on the station. PM coarse concentrations increase for increasing temperatures almost throughout the temperature range. Wind speed has a monotonic relationship to PM 2.5 except for one station, which exhibits a stationary point. Considering PM coarse , concentrations tend to increase or stabilize for large wind speeds at most stations. It was also observed that at all stations except one, higher PM 2.5 concentrations occurred for east wind direction, compared to west wind direction. Meteorologically adjusted PM time series were produced by removing most of the PM variability due to meteorology. It was found that PM 10 and PM 2.5 concentrations decrease at most stations. The average trends of the raw and meteorologically adjusted data are −0.4 μg m −3 yr −1 for PM 10 and PM 2.5 size fractions. PM coarse have much smaller trends and after averaging over all stations, no significant trend was detected at the 95% level of confidence. It is suggested that decreasing PM coarse in addition to PM 2.5 can result in a faster decrease of PM 10 in the future. The trends of the 90th quantile of PM 10 and PM 2.5 concentrations were examined by quantile regression in order to detect long term changes in the occurrence of very large PM concentrations. The meteorologically adjusted trends of the 90th quantile were significantly larger (as an absolute value) on average over all stations (−0.6 μg m −3 yr −1 ).
175 citations
Cites methods from "Linear Regression Analysis: Theory ..."
...The confidence interval of the slopes has been calculated using the t-statistic (Yan and Su, 2009)....
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