Statistical Analysis And Software Application
Mariu Carlton
The IBM SPSS Statistics software puts the power of advanced statistical analysis at your fingertips. Whether you are a beginner, an experienced analyst, a statistician or a business professional it offers a comprehensive suite of advanced capabilities, flexibility and usability that are not available in traditional statistical software.
With the user-friendly and intuitive interface of SPSS Statistics, you can easily manage and analyze large datasets, gaining actionable insights for data-driven decisions. Its advanced statistical procedures and modeling techniques enable you to optimize organizational strategies, including predicting customer behaviors, forecasting market trends, detecting fraud to minimize business risk and conducting reliable research to drive accurate conclusions.
Try the interactive product tour of SPSS Statistics to see how easily you can extract actionable insights to optimize your decisions. For an optimal experience, follow the modules in sequential order.
*Prices shown are indicative of one monthly user in USD, may vary by country, exclude any applicable taxes and duties or the cost of any add-ons and are subject to product offering availability in a locale.
The IBM SPSS software platform offers advanced statistical analysis, a vast library of machine learning algorithms, text analysis, open-source extensibility, integration with big data and seamless deployment into applications.
Within the SPSS software family of products, IBM SPSS Statistics supports a top-down, hypothesis testing approach to your data, while IBM SPSS Modeler exposes patterns and models hidden in data through a bottom-up, hypothesis generation approach.
BJS seeks to fund applications from state Statistical Analysis Centers under the fiscal year 2024 State Justice Statistics program. The program supports the collection, analysis, and dissemination of statistical information on crime and criminal justice at the state and local levels.
The Federal Criminal Case Processing Statistics (FCCPS) Tool is an interactive webtool that allows practitioners, policy makers, academics, and the general public to investigate and research various aspects about the federal criminal justice system. The FCCPS Tool enables the querying of data on the persons processed in the federal criminal justice system in nine cohorts across three case processing stages: (1) law enforcement, (2) prosecution/courts, and (3) incarceration. Additionally, see the Federal Criminal Case Processing Statistics (FCCPS) Tool Supplementary Information page for assistance with the tool.
Corrections Statistical Analysis Tool (CSAT) - Prisoners
This new and improved dynamic analysis tool allows you to examine national and jurisdictional prisoner data for both federal and state correctional authorities. You can view year-end populations, admissions, and releases by legal jurisdiction, physical custody in private facilities and local jails, imprisonment rate, citizenship status, prison capacity, juvenile or adult age group, and sex. The tool uses National Prisoner Statistics.
Corrections Statistical Analysis Tool (CSAT) - Prisoners (Resource Link)
Easy Access - Office of Juvenile Justice and Delinquency Prevention (OJJDP) data analysis tools
Easy Access is a family of web-based data analysis tools on juvenile crime and the juvenile justice system provided by the Office of Juvenile Justice and Delinquency Prevention (OJJDP). The applications provide information on national, state, and county population counts, as well as information on homicide victims and offenders, juvenile court case processing, and juvenile offenders in residential placement facilities.
Easy Access (Resource Link)
The Survey of Prison Inmates Data Analysis Tool (SPI DAT) is a new dynamic analysis tool that modernizes public access to the most recent SPI data (2016) with interactive visualizations. The SPI DAT allows users of all technical skill levels to readily analyze data, view selected charts, and create custom charts for a range of characteristics of the U.S. prison population. Filters can be selected to provide detailed results by specific characteristics. Users can choose to create data visualizations for persons in federal prisons, persons in state prisons, or all persons in U.S. prisons in 2016. Other modern features enable users to view additional statistics through the chart tooltip, display or hide chart footnotes, and download results.
National Crime Victimization Survey (NCVS) Application Programming Interface (API)
This tool provides access to National Crime Victimization Survey (NCVS) datasets via an Application Programming Interface (API) called Socrata Open Data API (SODA API). The API provides researchers and developers with end-points in multiple formats along with related codebooks, methodology, and metadata.
National Crime Victimization Survey (NCVS) Application Programming Interface (API) (Resource Link)
Fig. B1. Comparison of filter responses to initial data composed of impulses placed two units and eight units away from the right-hand boundary with (solid) and without (short dashed) the special end conditions described in appendix B. (a) Results for the first-order filter with a characteristic scale of five units, and (b) for the fourth-order filter with the same scale
Among the technical aspects of the recursive filters, the problems of achieving acceptable approximations to horizontal isotropy and the implementation of both periodic and nonperiodic boundary conditions that avoid the appearance of spurious numerical artifacts are treated herein. A multigrid approach that helps to minimize numerical noise at filtering scales greatly in excess of the grid step is also discussed. It is emphasized that the methods are not inherently limited to the construction of purely Gaussian shapes, although the detailed elaboration of methods by which a more general set of possible covariance profiles may be synthesized is deferred to the companion paper (Part II).
The numerical efficiency of these basic operators can also be turned to advantage within a statistical analysis scheme, specifically in the synthesis of the effective covariance-convolution operators needed by the descent algorithms of the large-scale linear solvers involved (Lorenc 1992). The Statistical Spectral Interpolation (SSI) of the National Centers for Environmental Prediction (NCEP) is an example of an analysis scheme in which the spectral representation of the background error covariance is employed directly (Parrish and Derber 1992). Methods of this type are inherently limited in their ability to deal conveniently with geographical inhomogeneities. Although one motivation of the present two-part study was to develop the tool of recursive filters to allow the operational three-dimensional variational analysis (3DVAR) scheme to accommodate spatial inhomogeneities in the background covariance, the inhomogeneous and anisotropic aspects of the filtering technique will be reserved for the companion paper (Purser et al. 2003, referred to henceforth as Part II); the focus of the present paper is to demonstrate the ability of appropriately constructed recursive filters to achieve acceptably isotropic covariance functions of Gaussian form. Part II will extend this study to more general non-Gaussian profiles and explore the case of spatially adaptive covariances of either locally isotropic or generally anisotropic forms.
A brief review of the ideas that underlie 3DVAR is given in section 2 in order to clarify the points at which the recursive filter plays a part. In section 3 we set forth the relevant theory pertaining to the construction of basic recursive filters capable of being forged into convolution operators reasonably representing the qualities desired by modeled covariance convolutions within an adaptive analysis scheme with a uniform Cartesian grid and with homogeneous covariances. Like the Gaussian covariances of Derber and Rosati (1989), which are obtained by multiple iterations of a diffusion operator, the basic recursive filters are crafted to produce approximately Gaussian smoothing kernels (but in fewer numerical operations than are typical in the explicit diffusion method). Some of the technicalities discussed in this section are treated in greater detail in the appendixes. In another paper, Wu et al. (2002), we provide examples of the applications of some of the techniques presented here to global variational analysis of meteorological data.
The theory of digital filtering was initially developed in the context of time series analysis. However, many of the same techniques are equally applicable in two or more spatial dimensions when the numerical grid is of a sufficiently regular configuration, as it usually is in numerical weather analysis. While we attempt to keep the technical discussion of this section self-contained, other related aspects of the topic of digital filter design are well covered by standard texts such as Otnes and Enochson (1972) and Papoulis (1984).
c80f0f1006