STATPAC: A General Purpose Package for Data Analysis and Fitting Statistical Models to Data

1980 ◽  
Vol 34 (1) ◽  
pp. 59 ◽  
Author(s):  
Stanley H. Strauss
Keyword(s):  
Data Analysis ◽  
Statistical Models ◽  
General Purpose

Microcomputers in Political Science

1983 ◽  
Vol 38 ◽  
pp. 1-9
Author(s):  
Herbert F. Weisberg
Keyword(s):  
Data Analysis ◽  
Political Science ◽  
Large Scale ◽  
Turnaround Time ◽  
General Purpose ◽  
Batch Mode ◽  
New Era ◽  
Large Scale Data ◽  
The Social ◽  
Frequency Counts

We are now entering a new era of computing in political science. The first era was marked by punched-card technology. Initially, the most sophisticated analyses possible were frequency counts and tables produced on a counter-sorter, a machine that specialized in chewing up data cards. By the early 1960s, batch processing on large mainframe computers became the predominant mode of data analysis, with turnaround time of up to a week. By the late 1960s, turnaround time was cut down to a matter of a few minutes and OSIRIS and then SPSS (and more recently SAS) were developed as general-purpose data analysis packages for the social sciences. Even today, use of these packages in batch mode remains one of the most efficient means of processing large-scale data analysis.

scholarly journals PAW, a general-purpose portable software tool for data analysis and presentation

1989 ◽  
Vol 57 (1-3) ◽  
pp. 432-437 ◽  
Author(s):  
René Brun ◽  
Olivier Couet ◽  
Carlo E. Vandoni ◽  
Pietro Zanarini
Keyword(s):  
Data Analysis ◽  
Software Tool ◽  
General Purpose ◽  
Portable Software

scholarly journals Evaluation and Comparison of Statistical and Conventional Methods of Forecasting Avalanche Hazard

1977 ◽  
Vol 19 (81) ◽  
pp. 375-387 ◽  
Author(s):  
P. Föhn ◽  
W. Good ◽  
P. Bois ◽  
C. Obled
Keyword(s):  
Data Analysis ◽  
Statistical Models ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Raw Data ◽  
Avalanche Forecasting ◽  
Avalanche Hazard ◽  
Forecasting Methods ◽  
Conventional Methods

Abstract Principal problems concerning the raw data and methodological limitations of statistical and conventional avalanche forecasting methods are summarized. The concepts of four statistical models based on multivariate data analysis, are outlined in a few words. In order to give an idea of the potential and quality of the different methods, test runs over two winters are discussed and a tentative store is established. Statistical models I and IV, together with the conventional forecast, attain a score of 70-80%, whereas statistical models II and III show a slightly poorer performance.

Microcomputers in Political Science

1983 ◽  
Vol 38 ◽  
pp. 1-9
Author(s):  
Herbert F. Weisberg
Keyword(s):  
Data Analysis ◽  
Political Science ◽  
Large Scale ◽  
Turnaround Time ◽  
General Purpose ◽  
Batch Mode ◽  
New Era ◽  
Large Scale Data ◽  
The Social ◽  
Frequency Counts

We are now entering a new era of computing in political science. The first era was marked by punched-card technology. Initially, the most sophisticated analyses possible were frequency counts and tables produced on a counter-sorter, a machine that specialized in chewing up data cards. By the early 1960s, batch processing on large mainframe computers became the predominant mode of data analysis, with turnaround time of up to a week. By the late 1960s, turnaround time was cut down to a matter of a few minutes and OSIRIS and then SPSS (and more recently SAS) were developed as general-purpose data analysis packages for the social sciences. Even today, use of these packages in batch mode remains one of the most efficient means of processing large-scale data analysis.

Fityk: a general-purpose peak fitting program

2010 ◽  
Vol 43 (5) ◽  
pp. 1126-1128 ◽  
Author(s):  
Marcin Wojdyr
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Powder Diffraction ◽  
Open Source Software ◽  
Strain Analysis ◽  
General Purpose ◽  
Powder Diffraction Data ◽  
Peak Fitting ◽  
Nonlinear Curve Fitting ◽  
Nonlinear Curve

Fitykis portable, open-source software for nonlinear curve fitting and data analysis. It specializes in fitting a sum of bell-shaped functions to experimental data. In particular, it enables Pawley refinement of powder diffraction data and size–strain analysis.

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