Statistical analysis was performed with SigmaPlot software 12.5 (Systat Software Inc., San Jose, CA). Statistical significance was calculated using unpaired Student’s t-test and one-way ANOVA, as adequate. p-values<0.05 were considered as significant.
Sigmaplot software 12
Manufactured by Grafiti LLC
Sourced in United States, Germany
SigmaPlot software 12.3 is a data analysis and graphing tool. It provides features for data manipulation, statistical analysis, and the creation of publication-quality graphs and plots.
Automatically generated - may contain errors
Lab products found in correlation
Spss software 18, by IBM (3 mentions)
Spss 17, by IBM (1 mentions)
Statistica software, by StatSoft (1 mentions)
Sigmaplot, by Grafiti LLC (1 mentions)
Spss for windows version 21, by IBM (1 mentions)
Calcusyn software version 2, by Biosoft (1 mentions)
Originpro 8, by OriginLab (1 mentions)
Spss for windows version 18, by IBM (1 mentions)
21 protocols using sigmaplot software 12
1
Statistical Analysis of Experimental Data
2
Species Differences Analyzed by ANOVA
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Significant differences among species were examined via one-way ANOVA in SPSS 18.0 (SPSS Inc., Chicago, IL, USA). The Bonferroni correction method was applied for the comparison statement because of the unequal sample size among groups. Curve fitting and plotting were conducted using Sigmaplot software 12.5 (Systat Software, San Jose, CA, USA).
3
Statistical Analysis of Experimental Data
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All data used in this study were expressed as mean, SD, and RSD (%) using Microsoft Excel 2021 software (Microsoft, Redmond, WA, USA). Statistical analysis was performed using SigmaPlot software 12.5 (Systat Software, Inc., San Jose, CA, USA).
4
Statistical Analysis of Pancreatic Immunohistochemistry
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Relationships between data from immunohistochemical stainings of pancreatic tissues were categorized and compared between groups by Fisher’s exact test using SPSS 17.0 (IBM, Ehningen, Germany). Statistical analysis of in vitro data was performed using SigmaPlot Software 12.5 (Systat Software GmbH, Erkrath, Germany). The Shapiro-Wilk test was used to test for normal distribution. Parametric data were analyzed by one-way RM ANOVA, while non-parametric data were analyzed by Kruskal-Wallis one-way ANOVA on ranks test. P-values < 0.05 were regarded as statistically significant and are indicated with an asterisk (*).
5
Statistical Analysis of Growth Inhibition
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Statistical analysis was performed using Statistica software (StatSoft, Tulsa, OK) or SigmaPlot software 12.5 (Systat Software GmbH, Erkrath, Germany). IC50 values of 50% growth inhibition were assessed by non-linear regression. Multi-group comparisons were performed by Kruskal-Wallis one-way analysis of variance (ANOVA) with Newman-Keuls posthoc test, alpha = 0.05. p values are expressed as *p < 0.05, **p < 0.01 and ***p < 0.001. Data are presented as means ± SEM (standard error of the mean) or SD (standard deviation) of N independent experiments or samples (N ≥ 3). Further details on each data set can be found in the respective figure legend.
6
Predictors of Pulmonary Hypertension in COPD
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The statistical analysis was done using Minitab 17.1.0.0 for windows (Minitab Inc., 2013, Pennsylvania, USA). Continuous date were presented as mean and SD, and categorical data as number and percent. The normality of data were examined using Shapilo Wilk test. Comparison between two continuous groups was done using independent Student's t-test and more than two groups with one-way ANOVA test. The two categorical groups or more were statistically analyzed using chi-square test. Pearson correlation coefficient test was used to examine the correlation between MPV and PASP in different COPD groups. Logistic regression analysis test with step wise elimination was used to predict the factors associated with PH, also the simple linear regression equation was estimated to predict PASP from MPV. The accuracy of MPV was assessed with receiver operating curve analysis, assuming that the area under the ROC curve of 0.9 for this study was significant with a margin of type I error 0.05 and type II error 0.1, the sample size calculated with a minimum total number of 70 and a minimum number of cases with PH of 35 using SigmaPlot software 12.5.0.38 for windows (SigmaPlot, Systat Software Inc. UK, 2011). All tests were two sided, P<0.05 was considered significant.
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The statistical analysis was done using Minitab 17.1.0.0 for windows (Minitab Inc., 2013, Pennsylvania, USA). Continuous date were presented as mean and SD, and categorical data as number and percent. The normality of data were examined using Shapilo Wilk test. Comparison between two continuous groups was done using independent Student's t-test and more than two groups with one-way ANOVA test. The two categorical groups or more were statistically analyzed using chi-square test. Pearson correlation coefficient test was used to examine the correlation between MPV and PASP in different COPD groups. Logistic regression analysis test with step wise elimination was used to predict the factors associated with PH, also the simple linear regression equation was estimated to predict PASP from MPV. The accuracy of MPV was assessed with receiver operating curve analysis, assuming that the area under the ROC curve of 0.9 for this study was significant with a margin of type I error 0.05 and type II error 0.1, the sample size calculated with a minimum total number of 70 and a minimum number of cases with PH of 35 using SigmaPlot software 12.5.0.38 for windows (SigmaPlot, Systat Software Inc. UK, 2011). All tests were two sided, P<0.05 was considered significant.
7
Analyzing Dye Decolorization Impact
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Results were analyzed statistically using Minitab 17.1.0.0 software (Minitab Inc., Pennsylvania, USA) and SigmaPlot Software 12.5.0.38 (SigmaPlot, Systat Software Inc., UK). The normality of data was estimated by the Shapiro-Wilk test. Comparisons between two or more groups were performed using ANOVA with Tukey-Kramer comparison test. Simple linear regression analysis was performed to evaluate the effect of dye and its metabolic products after decolorization on the viability of cells using a regression equation for prediction. The p < 0.05 is considered significant.
8
Quantitative Analysis of Wampee Extracts
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Statistical analysis was performed using SigmaPlot software 12.3 (Systat Software, Inc., Chicago, IL). Significance of relationships was calculated by multivariate method. Data were analyzed among groups using one-way analysis of variance (ANOVA) and Duncan's multiple comparison posttest using SPSS software 18.0 (SPSS Inc., Chicago, IL). Correlation analyses were performed on the matrix of the 15 quantitative parameters measured on wampee extracts with Pearson Correlation method using SigmaPlot software. P value less than 0.05 was considered statistically significant. All data were reported as mean ± SD of triplicate analyses.
9
Statistical Analysis of Experimental Data
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Statistical analyses were performed by Origin Pro 8 (Origin Lab Corporation, Northampton, MA, USA) and SigmaPlot software 12.3 (Systat Software, Inc., Chicago, IL, USA). Dose–effect analysis was performed using Calcusyn software version 2.0 (Biosoft, Cambridge, UK). Results were statistically analyzed among groups using one-way analysis of variance (ANOVA) and Ducan’s multiple comparison post-test. p-values less than 0.05 were regarded as statistically significant. Statistical analyses were performed using SPSS for Windows version 21.0 (SPSS Inc., Chicago, IL, USA). Correlation tests were performed using the Pearson’s correlation through SPSS 21.0.
10
Statistical Analysis of Experimental Data
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Statistical analyses were performed using SigmaPlot software 12.3 (Systat Software, Inc., Chicago, IL, USA). The significance of relationships was calculated by the multivariate method. Data were analyzed among groups using one-way analysis of variance (ANOVA) and Duncan’s multiple comparison post-test using SPSS software 18.0 (SPSS Inc., Chicago, IL, USA). P-values less than 0.05 were regarded as statistically significant. All data were reported as mean ± SD of triplicate analyses.
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