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Sigmastat for windows

Manufactured by IBM
Sourced in United States

SigmaStat for Windows is a statistical analysis software designed for Windows operating systems. It provides users with a suite of statistical tools for data analysis and modeling. The core function of SigmaStat is to assist researchers, scientists, and professionals in performing a variety of statistical tests and analyses on their data.

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16 protocols using sigmastat for windows

1

Comparative Statistical Analysis of Experimental Data

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One-way analysis of variance with the Bonferroni-Dunn test was used for multiple comparisons. Data were presented as mean ± standard deviation. The significance level was set at 0.05. A commercial software package (SigmaStat for Windows, SPSS Science, Chicago, IL) was used for data analysis.
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2

Soil 14C-Pyrene Mineralization Analysis

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Following blank-correction, statistical analysis of the results was performed in SigmaStat for windows (Version 2.03, SPSS Inc.). Significant effects on the mineralization of 14C-PYR in soil slurry (three different studied soils) and in inoculated and non-inoculated solution were compared statistically using a General Linear Model (ANOVA) (three replicates) (Tukey test, P < 0.05).
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3

Comparative Statistical Analysis Methodology

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The results are expressed as means ± standard errors (SEs). Statistical significance was determined using the p value (p < 0.05 or p < 0.01) through Student’s t test and one-way analysis of variance (ANOVA) on SigmaStat for Windows (version 2.03; SPSS, Chicago, IL, USA). Multiple comparisons were conducted using ANOVA with Tukey’s post hoc test.
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4

Protein Quantification Assay Protocol

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Results are expressed as means ± SE of at least three independent experiments. Significant differences between groups were determined by one‐way ANOVA and the Student t‐test. All statistical analyses were carried out using the statistical package SigmaStat for Windows (Version 2.03, SPSS, Chicago, IL).
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5

Quantitative Analysis of Gene Expression

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All values are given as mean ± SEM, with the number of samples in parentheses. Statistical comparisons were made using Student’s t test or, and when appropriate, analysis of variance and the Holm–Sidak method for multiple comparisons (p < 0.05 was considered significant) using log values for the qPCR and Western blot data. Calculations and curve fitting were performed using SigmaPlot and SigmaStat for Windows (SPSS Science, Chicago, IL).
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6

Statistical Analysis of Experimental Data

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All data were analyzed using Sigma Stat for windows (Version 3.0, SPSS Inc., Chicago, IL, USA). The data are expressed as the mean ± SEM. For comparisons among the groups, one-way analysis of variance (ANOVA) and Newman-Keuls multiple comparison tests were performed with P < 0.05 as an indication of statistical significance.
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7

Evaluating Therapeutic Efficacy in Animal Model

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All values are presented as mean ± SEM. Unless otherwise stated, the n values refer to the number of animals. Statistical comparisons were made using Student's t‐test or, when appropriate, analysis of variance (ANOVA) with post hoc testing for multiple comparisons using the Holm‐Sidak method or repeated measured for dependent variables. Calculations and curve fitting were performed using SigmaPlot and SigmaStat for Windows (SPSS Science, Chicago, IL).
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8

Analyzing Spinal Cord Reflexes

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Spinal Core software (Mor and Lev-Tov, 2007 (link)) was used to characterize/quantify the LPG output of the spinal cord induced by signal transmission from the brainstem via descending pathways and determine the correlations between the left phrenic and left peripheral muscle nerve discharges and between the left and right muscle nerve discharges during episodes of phrenic nerve discharge. Recorded data were high-pass filtered at 10–20 Hz, rectified, and compressed to a sample rate of 20 Hz. The time series data of left and right tibial nerve discharge and left phrenic and left tibial nerve discharge were used to compute the time domain between the lags from −1 to 1 s and from −2.5 to 2.5 s, respectively, and develop the correlation curves.
Statistical values are expressed as the mean (SE). Differences between means were analyzed using either a statistical software package (Sigma Stat for Windows; SPSS, Chicago, IL, USA) or excel and assessed by either one factor repeated measures ANOVA with a Scheffe post-hoc or the paired t-test. A P < 0.05 was considered to indicate statistical significance.
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9

Nonparametric Statistical Analysis of Data

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Due to the nonnormal distribution of the data, nonparametric tests were performed. Data were analyzed by Kruskal-Wallis analysis of variance and Dunn's multiple comparison test. All data are expressed as mean and SEM. Differences were considered significant at P < 0.05. A specific software package (SigmaStat for Windows version 3.00 SPSS Inc., San Jose, CA, USA) was used.
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10

Nonparametric Analysis of Experimental Data

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All of the data are expressed as means ± SDs. We analyzed our data with the nonparametric Mann-Whitney U test. The significance level was set at 0.05. A commercial software package (SigmaStat for Windows; SPSS Science, Chicago, IL) was used for the data analysis.
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