The evolution of a software system can be studied in terms of how various properties as reflected by software metrics change over time. Current models of software evolution have allowed for inferences to be drawn about certain attributes of the software system, for instance, regarding the architecture, complexity and its impact on the development effort. However, an inherent limitation of these models is that they do not provide any direct insight into where growth takes place. In particular, we cannot assess the impact of evolution on the underlying distribution of size and complexity among the various classes. Such an analysis is needed in order to answer questions such as 'do developers tend to evenly distribute complexity as systems get bigger?', and 'do large and complex classes get bigger over time?'. These are questions of more than passing interest since by understanding what typical and successful software evolution looks like, we can identify anomalous situations and take action earlier than might otherwise be possible. Information gained from an analysis of the distribution of growth will also show if there are consistent boundaries within which a software design structure exists. In our study of metric distributions, we focused on 10 different measures that span a range of size and complexity measures. The raw metric data (4 .txt files and 1 .log file in a .zip file measuring ~0.5MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).

This diploma thesis is focused on statistical processing data of medical rescue services of the Pilsen region. Data capture patients who have suffered a stroke. The main aim of this work is to provide an overview of statistical methods usable in the analysis of medical data. Another aim is to make appropriate data cleanup and create a graphical overview of the distribution of these data and variables bindings. Finally, the aim is to compare descriptive statistics and evaluate the possibility of using acquired knowledge to create predictive models. Tato diplomová práce se zaměřuje na statistické zpracování dat zdravotnické záchranářské služby Plzeňského kraje. Data zachycují pacienty, kteří prodělali cévní mozkovou příhodu. Hlavním cílem této práce je poskytnout přehled o statistických metodách využitelných při analýze medicínských dat. Dalším cílem je provést vhodné očištění dat a vytvořit grafický přehled o rozdělení těchto dat a vazbách proměnných. Posledním cílem je provést porovnání popisných statistik a zhodnotit možnost využití získaných znalostí k vytvoření prediktivních modelů. Obhájeno

It is a widely accepted fact that evolving software systems change and grow. However, it is less well-understood how change is distributed over time, specifically in object oriented software systems. The patterns and techniques used to measure growth permit developers to identify specific releases where significant change took place as well as to inform them of the longer term trend in the distribution profile. This knowledge assists developers in recording systemic and substantial changes to a release, as well as to provide useful information as input into a potential release retrospective. In order to manage the evolution of complex software systems effectively, it is important to identify change-prone classes as early as possible, but these analysis methods can only be applied after a mature release of the code has been developed. Specifically, developers need to know where they can expect change, the likelihood of a change, and the magnitude of these modifications in order to take proactive steps and mitigate any potential risks arising from these changes. We present a statistical analysis of change in approximately 55000 unique classes across all projects under investigation. The raw metric data (4 .txt files and 4 .log files in a .zip file measuring ~2MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).

The downloadable ZIP file contains an Esri grid. These data were created as part of a graduate thesis at the University of Idaho to 1.) demonstrate that a combination of geographic information systems (GIS) and multivariate statistical procedures can be used to map climate using data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM), and to 2). delineate agroclimate zones for the purpose of applying successful dryland agricultural research management practices throughout areas of relative climatic uniformity. No responsibility is assumed by Idaho State Climate Services in the use of these data.Multivariate statistical analysis and geographic information systems were used to delineate homogeneous agroclimate zones for Idaho for the purpose of applying successful dryland agricultural research practices and management decisions throughout these areas of relative climatic uniformity. Data used to produce the classification are from the Parameter-elevation Regressions on Independent Slopes Model (PRISM), developed at Oregon State University. PRISM has produced gridded estimates of mean monthly and annual climatic parameters from point data and a digital elevation model (DEM). Principal components analysis was performed on fifty-five variables including various temperature and precipitation parameters, the number of growing-degree days, the mean annual number of freeze-free days, the mean annual day of first freeze in the fall, and the mean annual day of last freeze in the spring. Cluster analysis was used to identify sixteen agroclimate zones each having similar climatic conditions regardless of its spatial location. As a result, successful dryland agricultural practices and management decisions that are based on new technologies and developed for one part of the state may potentially be applied to other parts of the state that fall within the same agroclimate zone.These data were created as part of this thesis: https://alliance-primo.hosted.exlibrisgroup.com/permalink/f/m1uotc/CP7117420067000145136" x 48" PDF map: https://insideidaho.org/data/ago/ics/agroclimate-zones.pdfThese data were contributed to INSIDE Idaho at the University of Idaho Library in 1999.

Historical wild fire records for the central western Cascades of Oregon are summarized here for the period from 1910 to 1977. Data records are obtained and summarized from historical statistical reports that were generated by the U.S. Forest Service and exist in various forms including fire maps, summary tables, and individual fire reports. The location, cause of the fire, its size class, and the source of information regarding each fire are included.