skip navigation


Register for Latest Research

Stay Informed
Register with NCJRS to receive NCJRS's biweekly e-newsletter JUSTINFO and additional periodic emails from NCJRS and the NCJRS federal sponsors that highlight the latest research published or sponsored by the Office of Justice Programs.

NCJRS Abstract

The document referenced below is part of the NCJRS Virtual Library collection. To conduct further searches of the collection, visit the Virtual Library. See the Obtain Documents page for direction on how to access resources online, via mail, through interlibrary loans, or in a local library.


NCJ Number: 97872 Find in a Library
Title: Forecasting Federal Probation Statistics
Journal: Federal Probation  Volume:48  Issue:4  Dated:(December 1984)  Pages:39-46
Author(s): S C Suddaby
Date Published: 1984
Page Count: 8
Sponsoring Agency: National Institute of Justice/
Rockville, MD 20849
NCJRS Photocopy Services
Rockville, MD 20849-6000
Sale Source: National Institute of Justice/
NCJRS paper reproduction
Box 6000, Dept F
Rockville, MD 20849
United States of America

NCJRS Photocopy Services
Box 6000
Rockville, MD 20849-6000
United States of America
Document: PDF
Type: Statistics
Language: English
Country: United States of America
Annotation: A computer model forecasting probation statistics based on Federal data, and variables for smaller users, is presented and discussed.
Abstract: A multiple regression model capable of forecasting the number of persons received for supervision has 5 predictor variables and will forecast 1 year ahead without having to forecast the predictor variables. The variables are the number of persons sentenced for 13- to 35-month terms, the number of persons sentenced to probation lagged 1 year and 2 years, the number of filed criminal cases lagged 3 years, and the statistical year ended June 30 of the year forecasted. The best predictor variables for forecasting the number of persons removed from supervision next year are the numbers currently under supervision and those under supervision in the prior 1, 2, and 3 years. These variables also are useful in forecasting the number of persons under supervision. Two forecasting models for persons under supervision are outlined. A third forecast for persons under supervision can be created by subtracting the forecast of persons removed and adding the forecast of persons received to the previous year's number under supervision. Short-term forecasts can be made by using the annual total every quarter as the variable which is forecast. If there has been a straight-line trend, then a linear regression permits extrapolation of this trend. If the trend is curved, parabolic regressions can be used to extrapolate a quarter or two ahead. Considerations in forecasting are discussed. These include the importance of graphing data, the trial and error nature of the process, the need for 20 to 25 years of historical data, back lagging, and serial correlation. Problems specific to probation data and their forecasting also are discussed. A list of statistics texts and 4 references are provided.
Index Term(s): Computer generated reports; Models; Prediction; Probation statistics; Regression analysis; Statistical analysis
To cite this abstract, use the following link:

*A link to the full-text document is provided whenever possible. For documents not available online, a link to the publisher's website is provided. Tell us how you use the NCJRS Library and Abstracts Database - send us your feedback.