Data were analyzed by the Kruskal-Wallis one-way analysis of variance (ANOVA) followed by a Dunn’s method pairwise multiple comparisons procedure to determine specific significant differences (p ≤ 0.05) using SigmaPlot 11 (SPSS, Inc., Chicago, IL). When appropriate, data are presented in figures and tables as means ± standard deviation (SD). Some important parameters (e.g., HOMA-IR, serum insulin levels) with non-linear distribution were log-transformed to assure a normal distribution pattern. Interactions between Cr(III) and BM were analyzed by the two-way ANOVA followed by Bonferroni’s test using SigmaPlot 11 (SPSS, Inc., Chicago, IL) to determine if there were significant interactions (p ≤ 0.05).
Sigma plot 11
Manufactured by IBM
Sourced in United States
SigmaPlot 11.0 is a data analysis and graphing software developed by Systat Software Inc. It provides a range of tools for data visualization, statistical analysis, and curve fitting. The software allows users to create high-quality scientific and engineering plots, charts, and graphs from their data.
Automatically generated - may contain errors
Lab products found in correlation
Clampfit 10, by Molecular Devices (2 mentions)
Coreldraw x5, by Corel (2 mentions)
Visplore 2, by Corel (2 mentions)
Sigmaplot 13, by Merck Group (2 mentions)
Spss 20, by IBM (1 mentions)
Spss statistics 19, by IBM (1 mentions)
Igor pro, by Wavemetrics (1 mentions)
Sigmastat 3, by IBM (1 mentions)
Prism 6, by GraphPad (1 mentions)
Ssoadvanced universal sybr green supermix, by Bio-Rad (1 mentions)
Fitmaster, by HEKA Elektronik (1 mentions)
Spss v25, by IBM (1 mentions)
49 protocols using sigma plot 11
1
Evaluating Chromium and Berberine Metabolic Interactions
2
Analytical and Biological Validation of Biomarkers
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Normality distribution of the data was tested using the Shapiro-Wilk test prior to statistical analysis. For the analytical validation, the parallelism between serial dilutions of two selected serum samples and standard curves was determined by a test of equality of two slopes using Logarithmic Regression
26 . For the biological validation, C data before and after transportation and T data of bulls and cows showed a normal distribution (p>0.05). A paired t-test was used for C, whereas for T, an independent t-test was used. For the freeze-thaw cycles experiments, the proportion of change in C and T concentrations relative to the control was calculated as (a
n– x
n/x
n) x 100, where a
n is the nth sample value in each freeze-thaw cycles series (two, four, six, eight times) and x
n is the value at time zero (control) of the nth sample
8 (link),
16 (link). The C data were normally distributed (p>0.05), whereas the T data was not (p<0.05). Therefore, for C, a one-way repeated measures ANOVA followed by
post hoc analysis using the Bonferroni test was conducted, whereas a Friedman repeated-measures ANOVA on ranks was set up to analyze T concentrations
8 (link). We used SigmaPlot 11.0 to create graphs and IBM SPSS 20 to carry out the statistical analysis. All statistical tests were two-tailed and the significance level was set at 0.05.
26 . For the biological validation, C data before and after transportation and T data of bulls and cows showed a normal distribution (p>0.05). A paired t-test was used for C, whereas for T, an independent t-test was used. For the freeze-thaw cycles experiments, the proportion of change in C and T concentrations relative to the control was calculated as (a
n– x
n/x
n) x 100, where a
n is the nth sample value in each freeze-thaw cycles series (two, four, six, eight times) and x
n is the value at time zero (control) of the nth sample
8 (link),
16 (link). The C data were normally distributed (p>0.05), whereas the T data was not (p<0.05). Therefore, for C, a one-way repeated measures ANOVA followed by
post hoc analysis using the Bonferroni test was conducted, whereas a Friedman repeated-measures ANOVA on ranks was set up to analyze T concentrations
8 (link). We used SigmaPlot 11.0 to create graphs and IBM SPSS 20 to carry out the statistical analysis. All statistical tests were two-tailed and the significance level was set at 0.05.
3
Standardized Muscle Strength and Endurance
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All results are presented as means ± SD or percentages. Comparison of baseline characteristics among the three centers was done by one-way analysis of variance (ANOVA). Variables that changed significantly from baseline to the end of the PRP were identified by paired t-tests in cases of normality of distribution of values or the Wilcoxon test if not normally distributed. All statistical analyses were performed using SigmaPlot 11.0 and SPSS Statistics 19 (IBM, Armonk, NY, USA). QMVC and the 6-minute walk distance (6MWD) measurements were standardized using z-scores. A z-score gives a quantified distance from the normative data in the SD in respect to the average reference performance of the population selected, which means that z-scores help us to compare the observed value of measurement to a normal population. A z-score of −1.64 signifies that the value measured lies in the fifth percentile.
The equation used was z-score = (Observed value – Predicted value)/SD. The predicted value for QMVC was taken from Hogrel et al34 and for 6MWD from Troosters et al.35 Statistical significance of comparisons was set at P<0.05.
The equation used was z-score = (Observed value – Predicted value)/SD. The predicted value for QMVC was taken from Hogrel et al34 and for 6MWD from Troosters et al.35 Statistical significance of comparisons was set at P<0.05.
4
Quantifying Protein Interactions via sFIDA
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Statistical analyses were performed using SigmaPlot 11.0, IBM SPSS Statistics 28.0.1.1 (15), OriginPro 2020 SR1, or JASP 0.14.1. Mean and standard deviation were calculated based on the sFIDA readout of four replicates. The intra-assay variation is described by the coefficient of variation (CV%). To determine inter-assay variation, the Spearman coefficient of correlation was calculated for the replicate measurements of the samples.
5
Statistical Analysis of Experimental Data
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For all experimental conditions, the results are represented as mean ± S.E.M. and were analyzed with ANOVA (for multicomparisons) and Students’s t-test (to compare just two groups) or the corresponding on rank tests in the case of non-parametric distributions, by using SigmaPlot 11 software (SPSS Science, Inc.) and critical values of: * P < 0.05, ** P < 0.01, and *** P < 0.001.
6
Neurophysiological Data Analysis Techniques
7
Statistical Analysis of Experimental Data
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Statistical analyses were performed using Sigma Plot 11 software (SPSS, Chicago, IL, USA). Data were analyzed for distribution by the Kolmogorov-Simirnov test. Equal variance and appropriate parametric - nonparametric tests (one way ANOVA with Tukey test as post hoc test or Kruskal Wallis with Dunn test as post hoc test) were used. Data were expressed as mean and standard error. The threshold value for acceptance of differences was 5%.
8
Robust Statistical Analysis of Experimental Data
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Analysis of variance (ANOVA) was done using SigmaPlot 11 (SPSS Science, USA) at a significance level of P < 0.05. For significant effects, a post-hoc all-pair wise comparison via Student-Newman-Keuls Method in one way ANOVA, and via Holm-Sidak method for two-way ANOVA was performed.
9
Statistical Analysis of Experimental Data
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Data were collected for each experimental condition from at least three independent experiments. To assess the statistical significance of intergroup differences (p<0.05), the standard two-tailed Student's t-test, or the one-way analysis of variance followed by Dunnett's test, was used. Results are presented as mean ± standard error of the mean (SEM). Statistical analysis and data plots were performed using SigmaPlot 11 software (SPSS Science, Chicago, IL, USA). Differences were considered significant when p<0.05.
10
Statistical Analysis of Experimental Data
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