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S2 Fig

April 2017

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Authors:

Gulu University

Norah P. Saarman

Utah State University

Gulu University

Elizabeth A. Opiyo

Gulu University

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Linear regression of allelic richness (microsatellite loci) and haplotype diversity (mtDNA) over longitude produced using JMP V11.0 (SAS Institute Inc., Cary, NC, USA, 1989–2007). Triangles, diamonds, and squares identify sampling sites within the Northwest, Transition Zone, Northeast genetic units, respectively. (PDF)

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Genetic diversity and population structure of the tsetse fly Glossina fuscipes fuscipes (Diptera: Glossinidae) in Northern Uganda: Implications for vector control

Article

April 2017

Robert Opiro · Norah P. Saarman · Richard Echodu · Elizabeth A. Opiyo · Adalgisa Caccone

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Figure S5

September 2016

Bivariate fit of estimate of effect of iBET72− or iBET72+ on 20861 and 20863 cells. Shown is a comparison of the effects of iBET72− or iBET72+ on 20861 and 20863 cells. Difference between treatment groups is shown in the log2 scale. Values for individual genes are shown in gray. Values for genes located on chromosome 2 (which contains integrated HPV16 locus) are shown in black. Linear fit ... [Show full abstract] analysis was performed in JMP v.12.0.1 (SAS Institutes). Linear fit represents estimate of iBET72+ effect on 20861 = 9.434e−6 + 0.9824377 × estimate of iBET72+ effect on 20863. R2 = 0.685992. Download

Article

Design of experiment and data analysis by JMP (R) (SAS institute) in analytical method validation

September 2000 · Journal of Pharmaceutical and Biomedical Analysis

Validation of an analytical method through a series of experiments demonstrates that the method is suitable for its intended purpose. Due to multi-parameters to be examined and a large number of experiments involved in validation, it is important to design the experiments scientifically so that appropriate validation parameters can be examined simultaneously to provide a sound, overall knowledge ... [Show full abstract] of the capabilities of the analytical method. A statistical method through design of experiment (DOE) was applied to the validation of a HPLC analytical method for the quantitation of a small molecule in drug product in terms of intermediate precision and robustness study. The data were analyzed in JMP (SAS institute) software using analyses of variance method. Confidence intervals for outcomes and control limits for individual parameters were determined. It was demonstrated that the experimental design and statistical analysis used in this study provided an efficient and systematic approach to evaluating intermediate precision and robustness for a HPLC analytical method for small molecule quantitation.

Chapter

Student Data and Problem 1

December 2016

Shuichi Shinmura

Student data consist of 40 students with six variables, which are the study hours per day (X1), spending money per month (X2), drinking days per week (X3), gender (X4), smoking (X5), and examination scores (X6). The amount of data is not large. We published four statistical books on SAS, SPSS, Statistica, and JMP using these data because the reader could easily understand the meaning of variables ... [Show full abstract] and data. Although we never believed the data would be helpful for our research, we discriminated the data after we completed analysis of the Iris, CPD, and random number data in 1999. When we discriminated these data using five variables with 70 points as the passing mark, we found a defect in IP-OLDF. Because four numerical variables are integer values and two variables are the binary integers 0/1, and there are many overlapping cases, the obtained vertex of convex polyhedron (CP) consists of over (p + 1) cases and the obtained solution is not true MNM. Although we recognized Problems 1 and 4 before 1980, we did not realize that Problem 1 causes a defect in IP-OLDF. By the scatter plot of two variables, as indicated in Table 1. 1, we found that the reason for the defect in IP-OLDF is the result of Problem 1. However, we could not revise this problem until 2006, when Revised IP-OLDF solved Problem 1 completely. In 2004, IP-OLDF found that Swiss banknote data are LSD, and no LDFs, with the exception of Revised IP-OLDF and H-SVM, could discriminate LSD theoretically (Problem 2 ). In 2005, we were able to validate the discrimination of original data (the training sample) by 20,000 resampling samples (the validation sample). After 2006, we could compare six MP-based LDFs and two statistical LDFs. After 2009, we developed the 100-fold cross-validation for small sample method (the Method 1). The Method 1 solves Problem 4 , and the best model provides clear evaluation of eight LDFs. Although we could not explain the useful meaning of 95 % CI of the coefficient, we completed the basic research in 2010. After 2010, applied research started on LSD discrimination using the pass/fail determination that employs examination scores. We find Problem 3 that was solved in 2013. In 2015, the applied research was completed because we could successfully explain the useful meaning of 95 % CI of the coefficient, and the Theory Method 1 solved Problem 4 completely. In October 2015, a young researcher, Ishii, presented the challenging results of microarray datasets using principal component analysis (PCA). Because the researcher indicated six microarray datasets on HP (http:// www. bioinf. ucd. ie/ people/ ian/ ), we developed the Matroska feature-selection method (Method 2) within 41 days. For more than ten years, many researchers have struggled to analyze microarray datasets because the datasets consist of few cases with huge genes (Problem 5 ). The Theory is most suitable for Problem 5 . Recently, many researchers have expected LASSO to solve Problem 5. Because Revised IP-OLDF selects features naturally, they should compare their results to ours through the Swiss banknote data, Japanese-automobile data, Student linearly separable data, and six microarray datasets. Such comparison should be helpful for LASSO research.

Article

New software for scientists and engineers

July 2007

J.A. Wass

SAS has introduced a new statistical software for scientists and engineers named JMP 7 that can be profitably and easily used by non-statisticians. The version 7 of JMP extends the variety of graphics to the dynamic linking of instant graphics to analysis of JMP. The new motion-enabled feature provides animation whereby variable changes may be instantly visualized and allows rapid recognition in ... [Show full abstract] patterns over time. The ability to integrate SAS fully into any analyses from JMP allows those analysts with access to both programs a new level of speed and flexibility while integrating the dynamic graphics of JMP with the breadth and depth of SAS' analytic features. As with JMP, the analyst can easily call up these visual manifestations of the equations, but now over the full range of Mathematica's considerable range of functions.