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2 edition of HIER-GRP, a computer program for the hierarchical grouping of regression equations found in the catalog.

HIER-GRP, a computer program for the hierarchical grouping of regression equations

C. Deene Gott

HIER-GRP, a computer program for the hierarchical grouping of regression equations

by C. Deene Gott

  • 135 Want to read
  • 5 Currently reading

Published by Air Force Human Resources Laboratory, Air Force Systems Command in Brooks Air Force Base, Tex .
Written in English

    Subjects:
  • Regression analysis

  • Edition Notes

    Statementby C. Deene Gott
    SeriesAFHRL-TR -- 78-14
    ContributionsAir Force Human Resources Laboratory. Computational Sciences Division
    The Physical Object
    Pagination68 p. :
    Number of Pages68
    ID Numbers
    Open LibraryOL14860565M

    A computer procedure for regression analysis that is used to identify the independent variables that are good predictors of the dependent variable in order to find the best fitting equation or model. Also called "stepdown selection.". Compare forward selection and stepwise regression. Jul 02,  · Most of the methodology in the basic Arc is based and described on the book, Applied Regression Including Computing and Graphics, by R. D. Cook and Sanford Weisberg, of the University of Minnesota. Arc is a user-friendly program designed specifically for use while studying this material, and for applying the ideas learned to other data sets/5().

    Controlling for variables in Hierarchical Regression? Some software calculate ANOVAs by subsequently adding predictors to the model and testing each model with the previous (the first one is. Start studying Lecture 9: Hierarchical, Moderated and Mediated Multiple Regression. Learn vocabulary, terms, and more with flashcards, games, and other study tools.

    May 23,  · Two sub-datasets were generated in order to examine the performance of the software packages when dealing with logistic random effects regression models on a smaller data set. Sample 1 (cases 2 and 5) consists of a simple random sample from the full data set and contains ogmaexpo.com by: Hierarchical Linear Modeling (HLM) Theoretical introduction Introduction to HLM HLM equations HLM interpretation of your data sets Building an HLM model Demonstration of HLM software Personal experience with HLM tutorial General Information and Terminology HLM can be used on data with many levels but we will only consider 2-level models The.


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HIER-GRP, a computer program for the hierarchical grouping of regression equations by C. Deene Gott Download PDF EPUB FB2

This description of the technical details required for using the HIER-GRP computer program, which was developed to group or cluster regression equations in an iterative manner so as to minimize the overall loss of predictive efficiency at each iteration, contains a discussion of the basic algorithm, an outline of the essential steps, specifications of the computer system requirements, descriptions of necessary control cards, and explanations of the program ogmaexpo.com by: 3.

HIER-GRP, a computer program for the hierarchical grouping of regression equations. [C Deene Gott; Air Force Human Resources Laboratory. Computational Sciences Division.]. HIER-GRP: A COMPUTER PROD AM FOR THE HIERARCHICAL GROUPING OF REGRESSION EQUATIONS.

IttffRODUCTION. H1ER-GRP, air acronym for hierarchical grouping, is a computer program which was developed for various Air Foro research purposes at the Computational Sciences Division, Air Force Human Resources Laboratotry, Brook AFB, Texas.

General applications of hierarchical grouping using the HIER-GRP computer program Author: Joe H Ward ; Bobby R Treat ; Walter G Albert ; Air Force Human Resources Laboratory. Jun 01,  · The authors wish to thank Robert Stack, Division Manager of Planning and Research, Equibank, N.

(Pittsburgh) and George Day (University of Toronto), for their valued comments and criticisms. Thanks also to Walter McGhee (University of Pittsburgh) for making available the computer Cited by: HIER-GRP is a computer program which has been used extensively to perform this kind of hierarchical grouping on regression equations which satisfy a proportionality condition.

This paper describes a method for using HIER-GRP, without modification, to perform grouping. General applications of hierarchical grouping using the HIER-GRP computer program [microform] / by Joe MASHIT-For Ease in Regression Program Communication [microform] / Robert L.

Mason and Keith A. McNeil; Computer-Aided Techniques for Providing Operator Performance Measures [microform] / Edward M. Connelly a. Multiple hierarchical regression analysis was used to generate prediction equations for all of the calculated WASI–II and WAIS–IV indexes.

The TOPF with simple demographics is the only model presented here and it applies only to individuals aged 20 to effects modeling, hierarchical linear modeling, multilevel modeling, linear mixed modeling, growth modeling, and longitudinal modeling.

Linear mixed models in some disciplines are called “random effects” or “mixed effects” models. In economics, the term “random coefficient regression models” is. i;/  p.jx. i/ (5) The distribution of a parameter for a totally unobserved group new will depend on the posterior of hyperparameter conditioned on all the data.

3Hierarchical regression One of the main application areas of hierarchical modeling is to regression and generalized linear models. Topics in regression analysis / Arthur S. Goldberger; General applications of hierarchical grouping using the HIER-GRP computer program [microform] / by Joe The idea of decline in Western history / Arthur Herman; Seasonal variability in a distributed lag model / [by] P.K.

Trivedi and B.M.S. Lee. Package ‘ogmaexpo.com’ February 25, Type Package Title Hierarchical Partitioning Description Partitioning of the independent and joint contributions of each variable in a multivariate data set, to a linear regression by hierarchical decomposition of goodness-of-fit measures of regressions using all subsets of predictors in the data set.

groups (for the hyperparameter) and on other data within the same group (for the per-group parameter). – The distribution of a parameter for a totally unobserved group „new will depend on the posterior of hyperparameter µ conditioned on all the data. 4 Hierarchical regression. Stepwise versus Hierarchical Regression, 7 A colleague of the present author noted that one could also imagine a different type of team being brought together to work on a common goal.

For example, a team of the smartest people in an organization might be selected in a stepwise manner to produce a report of cutting edge research in their field. the groups of variables should be entered in, and then let the computer decide within each group what the sequencing should be.

This type of analysis is likely to be primarily hierarchical (between classes of IVs) and only incidentally stepwise (within classes). Analytic Strategies: Simultaneous, Hierarchical, and Stepwise Regression - Page 3.

Multiple regression can be performed by entering all of the predictors of interest in one step or by using a hierarchical method in which the researcher enters the predictors in some predetermined manner either one at a time or in groups.

There are also a number of indirect methods where the. • The proportion of total variance that is between the groups of the regression equation • “The degree to which individuals share common experiences due to closeness in space and/or time” Kreft & de Leeuw, • a.k.a – ICC is the proportion of group-level variance to the total variance • LARGE ICC DOES NOT EQUAL LARGE.

Forward stepwise regression programs are designed to select from a group of predictors the one variable at each stage which has the largest semi-partial r-squared, and hence makes the largest contribution to R-squared. (This will also be the variable that has the largest T value.). Multilevel models are particularly appropriate for research designs where data for participants are organized at more than one level (i.e., nested data).

The units of analysis are usually individuals (at a lower level) who are nested within contextual/aggregate units (at a higher level). Hierarchical linear modeling allows you to model nested data more appropriately than a regular multiple linear regression. Hierarchical regression, on the other hand, deals with how predictor (independent) variables are selected and entered into the model.

Nov 27,  · I demonstrate how to perform and interpret a hierarchical multiple regression in SPSS. I pay particular attention to the different blocks associated with a hierarchical multiple regression, as.Hierarchical Regression in Stata: An Easy Method to Compare Model Results.

by Jeff Meyer. by Jeff Meyer. I can easily re-run model 4, using a different reference group base category each time. I ran three sets of multiple regression equations. The IVs are the same while the DVs are broken into quantity, quality and combined (quantity.Structural Equation Modeling Using AMOS 3 The Division of Statistics + Scientific Computation, The University of Texas at Austin Section 1: Introduction About this Document/Prerequisites This course is a brief introduction and overview of structural equation modeling using the AMOS (Analysis of Moment Structures) software.