The purpose of this page is to make users aware of the latest versions andupdates to statistical software that is commonly used at UCLA. A shortlist of free statistical software is provided at the end of this page. Forthe latest updates for those programs, please visit the link provided.
Statistical analysis, in terms of descriptive statistics, P-Value of concordance test and P-Value of correlation, has been performed with MINITAB 15.1 software. Logistic regression tool available in the software was adopted to investigate the relationship between a response variable and one or more predictors when ordinal and/or nominal categories were of interest.
The 6 panels plot index scores v. θ for the 5 main health-related quality-of-life (HRQoL) indexes and the Health Activities Limitations Index (HALex). Horizontal and vertical banding for the 3844 data points in each plot represents discrete attainable levels for the index or derived θs. The heavy lines are points fit using Minitab®15.1 statistical software and the locally weighted scatterplot smoothing (LOWESS) option, a variant of kernel smoothing in the vertical dimension of scatterplots. The LOWESS lines demonstrate nonlinearity of the relation between the index scales and θ.
The six panels plot index scores versus Relative θ for the 5 main HRQoL indexes and the HALex. The heavy lines are points fit using Minitab®15.1 statistical software and the locally weighted scatterplot smoother (LOWESS) option.
JMP software is partly focused on exploratory data analysis and visualization. It is designed for users to investigate data to learn something unexpected, as opposed to confirming a hypothesis. JMP links statistical data to graphics representing them, so users can drill down or up to explore the data and various visual representations of it. Its primary applications are for designed experiments and analyzing statistical data from industrial processes. JMP can be used in conjunction with the R and Python open source programming languages to access features not available in JMP itself.
As you do research with larger amounts of data, it becomes necessary to graduate from doing your data analysis in Excel and find a more powerful software. It can seem like a really daunting task, especially if you have never attempted to analyze big data before. There are a number of data analysis software systems out there, but it is not always clear which one will work best for your research. The nature of your research data, your technological expertise, and your own personal preferences are all going to play a role in which software will work best for you. In this post I will explain the pros and cons of Stata, R, and SPSS with regards to quantitative data analysis and provide links to additional resources. Every data analysis software I talk about in this post is available for University of Illinois students, faculty, and staff through the Scholarly Commons computers and you can schedule a consultation with CITL if you have specific questions.
Data are presented as mean ± SD. Statistical analysis was conducted using the software package Minitab (version 18.104.22.168; Minitab, State College, PA). Significance was assessed by Student's t test and one-way ANOVA followed by Bonferroni t test for the comparisons of multiple means, or by Kruskal-Wallis test and subsequent pair-wise comparisons. The p value was considered to be statistically significant when less than 0.05. 2b1af7f3a8