Hasil untuk "Analysis"

J. Hilbe

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D. Mackinnon

Andreas Karlsson

Helmut Ltkepohl

W. Powell, Paul DiMaggio

J. Swales

Ziheng Yang

T. Murata, Chang-Ching Wu

J. Singer, J. Willett

Richard A. Johnson, D. W. Wichern

S. Skogestad, I. Postlethwaite

A. Agresti

F. Damanpour

K. Vehkalahti, B. Everitt

ەی اكتره . Abstract This study was conducted with the aim to identify the most important effective factors that lead to increase the car accidents in Erbil Governorate, by classifying the variables to the homogeneous groups, and identifying the most important variables, also to find the effective of the variables in the studied phenomenon. Later two types of Multivariate Analysis; the Factor Analysis by Using Principle components method and Cluster Analysis by Using Ward method have been applied as a tools to analyzing the data, then the comparison was made between the two analysis . The most important result for this study is, classifying the variables that caused the increasing of car accidents to eight significant factors and explained the largest part of the variance

J. Shawe-Taylor, N. Cristianini

W. Härdle, L. Simar

Chris Lattner, Vikram S. Adve

R. E. Donatelli, Ji-Ae Park, Spencer M. Mathews et al.

INTRODUCTION This article describes a simple method of applying a time series analysis to sample data sets using a free and open statistical software program, Language R. METHODS Records of new patients who visited 2 different university-affiliated orthodontic departments in 2 different countries were collected. Time series analysis was performed by applying Language R software. The data sets and codes were provided for tutorial and illustrative purposes. RESULTS Using time series decomposition, the trend component and the seasonal variation were separated and visualized graphically. CONCLUSIONS Time series analysis may be helpful to clinicians by providing a simple tool to evaluate patient characteristics and manage the practice.

S. Uppala, P. Kållberg, A. Simmons et al.

M. Lipsey, David B. Wilson