UNdata Global Engineering Services Market Research Industry Analysis & Industry Trends The Global Engineering Services industry provides surveys, feasibility and technical studies, geo-technical investigation and environmental impact studies for transportation, building, energy, industrial, mining and water and sewage industries. Typical industry projects involve the design and construction of new factories, healthcare facilities, hotels and housing; the building of highways, bridges, railways and sewage treatment facilities; and analysis and planning related to drilling, mining, energy transmission and storage. This industry's performance is principally driven by trends in construction, manufacturing and mining developments and increasingly from work in the fields of technological development and sustainability... read more Industry Key Buyers The industry has a low degree of market share concentration, with the four largest companies contributing to less than 5.0% of annual global industry revenue.
Data.gov 6259.0 - Information Paper: Revisions to Historical ANZSIC Industry Data, 2011 This publication provides details of revisions to the concordance between the formerly used Australian Standard Industrial Classification (ASIC 1983) and ANZSIC 1993. It replaces the previous issue of this information paper (released 28 June 1996) in order to provide amendments to a small number of Industry Groups that were inappropriately revised in 1996. An improved concordance has now been applied to historical labour force estimates for the period November 1984 to May 1994. From February 2011, these revisions replace the historical time series data published in Labour Force, Detailed, Quarterly (cat. no. 6291.0.55.003). This new concordance remains largely the same as the one detailed in the previous issue of this publication. Amendments have only been made to the estimated employment ratio between 7 ASIC 1983 Industry Groups and their 18 attributable ANZSIC 1993 Groups. The following table provides the concordance details of a further 7 ASIC 1983 Groups. Table 04.
Timetric: Making data useful IT Industry Competitiveness Index 2011 Portail de la statistique publique Four assumptions of multiple regression that researchers should always test. Osborne, Jason & Elaine Waters Jason W. Osborne and Elaine WatersNorth Carolina State University and University of Oklahoma Most statistical tests rely upon certain assumptions about the variables used in the analysis. When these assumptions are not met the results may not be trustworthy, resulting in a Type I or Type II error, or over- or under-estimation of significance or effect size(s). As Pedhazur (1997, p. 33) notes, "Knowledge and understanding of the situations when violations of assumptions lead to serious biases, and when they are of little consequence, are essential to meaningful data analysis". Several assumptions of multiple regression are “robust” to violation (e.g., normal distribution of errors), and others are fulfilled in the proper design of a study (e.g., independence of observations). Regression assumes that variables have normal distributions. Bivariate/multivariate data cleaning can also be important (Tabachnick & Fidell, p 139) in multiple regression. Reliability and simple regression Jason W.