Statistical analysis and graphical display of multivariate data on the Macintosh

1989 ◽  
Vol 5 (4) ◽  
pp. 287-292 ◽  
Author(s):  
J. Thioulouse
Keyword(s):  
Statistical Analysis ◽  
Multivariate Data ◽  
Graphical Display

scholarly journals Development of a PC Program for Multivariate Statistical Analysis

2017 ◽  
Vol 27 (4) ◽  
pp. 291 ◽  
Author(s):  
Pham Ngoc Son ◽  
Cao Dong Vu ◽  
Mai Quynh Anh
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Nuclear Research ◽  
Multivariate Statistical ◽  
Nuclear Research Institute ◽  
Data Set ◽  
Analysis Techniques ◽  
Preliminary Version

This report introduces a new computer program, having been developed initially at the Nuclear Research Institute at Dalat, for the multivariate data analysis techniques. In this preliminary version of the program, the size of a given data set to be analyzed is up to 50 variables and thousand observations, and can be used to perform some of the multivariate data analysis techniques such as principle component analysis, cluster analysis and data standardization. In comparison with other statistical analysis software, the same results are highly reproduced with MSAP.

A computer program for analyzing enzyme kinetic data using graphical display and statistical analysis

1984 ◽  
Vol 17 (3) ◽  
pp. 289-301 ◽  
Author(s):  
Serge D. Schremmer ◽  
Mark R. Waser ◽  
Michael C. Kohn ◽  
David Garfinkel
Keyword(s):  
Statistical Analysis ◽  
Computer Program ◽  
Kinetic Data ◽  
Graphical Display ◽  
Enzyme Kinetic

scholarly journals Efficiency assessment of e-learning compared to traditional education using Principal Component Analysis and verification of Genetic Algorithms: تقييم كفاءة التعليم الإلكتروني مقارنة بالتعليم التقليدي باستخدام تحليل المكونات الأساسية والتحقق بالخوارزميات الجينية

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Firas Shawkat Hamid
Keyword(s):  
Principal Component Analysis ◽  
Genetic Algorithms ◽  
Factor Analysis ◽  
Statistical Analysis ◽  
Technological Development ◽  
Multivariate Data ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
E Learning

Multivariate data analysis is one of the common techniques that are used in the analysis of the main compounds that perform the process of converting a large number of related variables into a smaller number of unrelated compounds, In the case of the emergence of anomalous values, which can be detected in many ways, the adoption of the matrix of contrast and common contrast will lead to misleading results in the analysis of the principal compounds. Therefore, many of the phenomena that consist of a large group of variables that are difficult to deal with initially, and the process of interpreting these variables becomes a complex process, so reducing these variables to a lower setting is easier to deal with, and it is the aspiration of every researcher working in the field of main compounds analysis or factor analysis. Because of technological development and the ability to communicate by audio and video interaction at the same time, on this research, a multivariate data collection process was conducted, where an evaluation of the efficiency of e-learning was studied and analyzed by highlighting the process of analyzing real data using factor analysis by the Principal Component Analysis method. This is one of the techniques used to summarize and shorten the data and through the use of the SPSS: Statistical Packages for Social Sciences Program, Thus, it will be noted that the subject of the paper will flow into the concept of Data mining also, And then achieve it using genetic algorithms using the simulation program with its final version, which is MATLAB, also using the method of Multiple Linear Regression Procedure to find the arrangement of independent variables by calculating the weight of the independent variable. Total results were obtained for the eigenvalues of the stored correlation matrix or the rotating factor matrix, The study required conducting statistical analysis in the mentioned way and by reducing the number of variables without losing much information about the original variables and its aim is to simplify its understanding and reveal its structure and interpretation, The study required conducting statistical analysis in the mentioned way and by reducing the number of variables without losing much information about the original variables and its aim is to simplify its understanding and reveal its structure and interpretation. In addition to reaching a set of conclusions that were discussed in detail also the addition to the important recommendations.

Practical Data Analysis

2015 ◽  
pp. 191-216
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Multivariate Data Analysis ◽  
Quantitative Data ◽  
Multivariate Data ◽  
Statistical Techniques ◽  
Analysis Techniques ◽  
Data Coding

In this chapter, students will learn “what to do” with their quantitative data once it has been collected. The chapter begins with a discussion of data coding, which is the process of preparing one's data for statistical analysis. What follows is a discussion of basic univariate, bivariate, and multivariate data analysis techniques. These techniques are presented in such a way that students with limited statistics backgrounds can understand and employ. Emphasis in this chapter is placed on giving students a working knowledge of statistical techniques that are most widely used when interpreting quantitative data.

Statistical analysis of multivariate data

2011 ◽  
pp. 151-403 ◽  
Author(s):  
Milan Meloun ◽  
Jiří Militký
Keyword(s):  
Statistical Analysis ◽  
Multivariate Data

Statistical analysis of classification

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Classification Scheme ◽  
Analytical Procedure ◽  
Narrow Band ◽  
Scientific Research ◽  
Maximum Amount ◽  
Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

A New AID for Interpolating and Assessing Collision Strengths and Rate Coefficients

1988 ◽  
Vol 102 ◽  
pp. 107-110
Author(s):  
A. Burgess ◽  
H.E. Mason ◽  
J.A. Tully
Keyword(s):  
Significant Loss ◽  
Impact Excitation ◽  
Rate Coefficients ◽  
Electron Impact Excitation ◽  
Graphical Display ◽  
Practical Procedure ◽  
Positive Ions ◽  
Allowed Transition ◽  
Computational Errors ◽  
Existing Data

AbstractA new way of critically assessing and compacting data for electron impact excitation of positive ions is proposed. This method allows one (i) to detect possible printing and computational errors in the published tables, (ii) to interpolate and extrapolate the existing data as a function of energy or temperature, and (iii) to simplify considerably the storage and transfer of data without significant loss of information. Theoretical or experimental collision strengths Ω(E) are scaled and then plotted as functions of the colliding electron energy, the entire range of which is conveniently mapped onto the interval (0,1). For a given transition the scaled Ω can be accurately represented - usually to within a fraction of a percent - by a 5 point least squares spline. Further details are given in (2). Similar techniques enable thermally averaged collision strengths upsilon (T) to be obtained at arbitrary temperatures in the interval 0 < T < ∞. Application of the method is possible by means of an interactive program with graphical display (2). To illustrate this practical procedure we use the program to treat Ω for the optically allowed transition 2s → 2p in ArXVI.

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