Environmental impact of warehousing: a scenario analysis for the United States

In recent years, there has been observed a continued growth of global carbon dioxide emissions, which are considered as a crucial factor for the greenhouse effect and associated with substantial environmental damages. Amongst others, logistic activities in global supply chains have become a major cause of industrial emissions and the progressing environmental pollution. Although a significant amount of logistic-related carbon dioxide emissions is caused by storage and material handling processes in warehouses, prior research mostly focused on the transport elements. The environmental impact of warehousing has received only little attention by research so far. Operating large and highly technological warehouses, however, causes a significant amount of energy consumption due to lighting, heating, cooling and air condition as well as fixed and mobile material handling equipment which induces considerable carbon dioxide emissions. The aim of this paper is to summarise preliminary studies of warehouse-related emissions and to discuss an integrated classification scheme enabling researchers and practitioners to systematically assess the carbon footprint of warehouse operations. Based on the systematic assessment approach containing emissions determinants and aggregates, overall warehouse emissions as well as several strategies for reducing the carbon footprint will be studied at the country level using empirical data of the United States. In addition, a factorial analysis of the warehouse-related carbon dioxide emissions in the United States enables the estimation of future developments and facilitates valuable insights for identifying effective mitigation strategies

An Introductory Business Statistics Course: Evaluating its Long-Term Impact and Suggestions for its Improvement

Undergraduate pre-business students who had taken an introductory business statistics course three years ago were sent a questionnaire that, among several other questions, asked them about their views on the usefulness of the course. Students rated the introductory business statistics course as "moderately important" for their business education. They favored an integrated approach that covers both the statistical concepts and the computer software necessary to carry out the statistical analysis, and they had a strong preference for the Microsoft EXCEL software. Students thought that projects played an important role in introducing them to real-world applications of statistics, but they also mentioned several problems that arose with group-based work. Factors mentioned as having had an impact on teaching effectiveness are discussed in the last part of the paper. (author's abstract)Series: Forschungsberichte / Institut für Statisti

Multi-Unit Longitudinal Models with Random Coefficients and Patterned Correlation Structure: Modelling Issues

The class of models which is studied in this paper, multi-unit longitudinal models, combines both the cross-sectional and the longitudinal aspects of observations. Many empirical investigations involve the analysis of data structures that are both cross-sectional (observations are taken on several units at a specific time period or at a specific location) and longitudinal (observations on the same unit are taken over time or space). Multi-unit longitudinal data structures arise in economics and business where panels of subjects are studied over time, biostatistics where groups of patients on different treatments are observed over time, and in situations where data are taken over time and space. Modelling issues in multi-unit longitudinal models with random coefficients and patterned correlation structure are illustrated in the context of two data sets. The first data set deals with short time series data on annual death rates and alcohol consumption for twenty-five European countries. The second data set deals with glaceologic time series data on snow temperature at 14 different locations within a small glacier in the Austrian Alps. A practical model building approach, consisting of model specification, estimation, and diagnostic checking, is outlined. (author's abstract)Series: Forschungsberichte / Institut für Statisti

Data mining and business analytics with R

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Minin

Statistical methods for forecasting

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists."This book, it must be said, lives up to the words on its advertising cover: ''Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice.'' It does just that!"-Journal of the Royal Statistical Society"A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series analysis by PhD students; or to support a concentration in quantitative methods for MBA students; or as a work in applied statistics for advanced undergraduates."-ChoiceStatistical Methods for Forecasting is a comprehensive, readable treatment of statistical methods and models used to produce short-term forecasts. The interconnections between the forecasting models and methods are thoroughly explained, and the gap between theory and practice is successfully bridged. Special topics are discussed, such as transfer function modeling; Kalman filtering; state space models; Bayesian forecasting; and methods for forecast evaluation, comparison, and control. The book provides time series, autocorrelation, and partial autocorrelation plots, as well as examples and exercises using real data. Statistical Methods for Forecasting serves as an outstanding textbook for advanced undergraduate and graduate courses in statistics, business, engineering, and the social sciences, as well as a working reference for professionals in business, industry, and government

A Statistical Analysis of the Water Levels at Lake Neusiedl

The recent low water levels of Lake Neusiedl have raised concern about the lake's future and have sparked interest on how meteorological variables impact the water level. Data from the last 50 years are used to study the impact of rain and temperature on the cyclical changes of the water levels. Linear and nonlinear regression models are used to describe how rainfall and temperature change the water level, and to assess the future progression of the water level under three scenarios. For average 2022 meteorological conditions the water levels in 2022 may not recover for a dry 2021 autumn, but will recover substantially for a wet ending to the year 2021

Analysis of Multi-Unit Variance Components Models with State Space Profiles

Continuous-time stochastic models, EM algorithm, Kalman Filter, mixed model prediction, restricted maximum likelihood, smoothing splines, unequally spaced observations, variance components,

MIXED MODEL REPRESENTATION OF STATE SPACE MODELS: NEW SMOOTHING RESULTS AND THEIR APPLICATION TO REML ESTIMATION

Abstract: Known results for the general linear mixed model and its special case, the variance components model, are applied to inference in state space models. New state and disturbance smoothing algorithms that accomodate fixed effects and diffuse initial conditions are developed. The algorithms are based on an augmented Kalman filter, and they avoid the backward recursions of standard smoothing algorithms. The disturbance smoother is used to develop an EM algorithm for REML estimation of variance components in state space models. The EM algorithm for the structural time series model with polynomial trend and additive seasonality is illustrated in detail