Practical Data Analysis

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.

2017 ◽  
Vol 27 (4) ◽  
pp. 291 ◽  
Author(s):  
Pham Ngoc Son ◽  
Cao Dong Vu ◽  
Mai Quynh Anh

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.


2005 ◽  
Vol 19 (6) ◽  
pp. 2350-2356 ◽  
Author(s):  
Vinicius L. Skrobot ◽  
Eustáquio V. R. Castro ◽  
Rita C. C. Pereira ◽  
Vânya M. D. Pasa ◽  
Isabel C. P. Fortes

Sign in / Sign up

Export Citation Format

Share Document