Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Publication
- Publication Type
Articles 1 - 3 of 3
Full-Text Articles in Applied Statistics
Explicit Equations For Acf In Autoregressive Processes In The Presence Of Heteroscedasticity Disturbances, Samir Safi
Explicit Equations For Acf In Autoregressive Processes In The Presence Of Heteroscedasticity Disturbances, Samir Safi
Journal of Modern Applied Statistical Methods
The autocorrelation function, ACF, is an important guide to the properties of a time series. Explicit equations are derived for ACF in the presence of heteroscedasticity disturbances in pth order autoregressive, AR(p), processes. Two cases are presented: (1) when the disturbance term follows the general covariance matrix, Σ , and (2) when the diagonal elements of Σ are not all identical but σi,j = 0 ∀i ≠ j.
Data Analysis Using Experimental Design Model Factorial Analysis Of Variance/Covariance (Dmaovc.Bas), Wesley E. Newton
Data Analysis Using Experimental Design Model Factorial Analysis Of Variance/Covariance (Dmaovc.Bas), Wesley E. Newton
All Graduate Theses and Dissertations, Spring 1920 to Summer 2023
DMAOVC.BAS is a computer program written in the compiler version of microsoft basic which performs factorial analysis of variance/covariance with expected mean squares. The program accommodates factorial and other hierarchical experimental designs with balanced sets of data. The program is writ ten for use on most modest sized microprocessors, in which the compiler is available. The program is parameter file driven where the parameter file consists of the response variable structure, the experimental design model expressed in a similar structure as seen in most textbooks, information concerning the factors (i.e. fixed or random, and the number of levels), and necessary …
Factorial Analysis Of Variance And Covariance On A Minicomputer, Ladonna Black Kemmerle
Factorial Analysis Of Variance And Covariance On A Minicomputer, Ladonna Black Kemmerle
All Graduate Plan B and other Reports, Spring 1920 to Spring 2023
Statistical analysis of large data sets is commonly performed on computers using one of the many available programs. Most of these programs have been written for computers with internal storage large enough to handle nearly any data set. Recently, however, there has been a trend to computers with more limited storage capabilities. New programs must be written or old programs adapted so that large data sets may also be analyzed on these smaller machines.
This report describes a program to analyze data from a balanced experiment of crossed and/or nested design. It was written for the Data General Nova minicomputer …