Statistical analysis of the gene expression data generated by RT-qPCR was performed using SAS JMP Pro 13.0.0 software and the data are presented as mean ± SD and p < 0.05 was considered to be significant. Data were analyzed using Student t test (two-tailed). Linear regression analysis of ACF data was performed using Stata 15.0 software. Regression analysis of macroscopic polyp neoplasms was determined by Poisson regression, which was performed using Stata 15.0 software.
Sas jmp pro 13
Manufactured by SAS Institute
Sourced in United States
SAS JMP Pro 13 is a data analysis and visualization software that provides advanced statistical capabilities. It offers a range of tools for exploring, modeling, and understanding data. The software is designed to help users gain insights and make data-driven decisions.
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Lab products found in correlation
8 protocols using sas jmp pro 13
1
Statistical Analysis of Gene Expression
2
Comprehensive Cultivar Analysis via ANOM and Clustering
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All the statistical analyses were done using SAS JMP Pro 13.0.0 (2016). Analysis of Means (ANOM) (Mendeş and Yiğit, 2018 ) was conducted for IAD and color (hue) results of all the cultivars. This analysis took into account all the data points from each cultivar. The average line represents the values of all the data points. The upper and lower decision limits (UDL and LDL) are the values for each cultivar that above or below that, respectively, deviates from the average at p ≤ 0.001. All the one-way ANOVA tests and the different letters of significance were acquired by the Tukey-Kramer HSD test at p ≤ 0.05 following a normal distribution test. Correlations were established by means of the Pearson correlation coefficient (R). ClustVis online tool2 was used to create a heat-map for all parameters of 2017 and 2019. The data for the heat map was normalized as a percentage of the average values of each parameter.
3
Nutrient Removal Optimization in Mesocosms
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N values reported are the sum of ammonium-N, nitrate-N, and nitrite-N. p values reported are phosphate-P. Water sample results were converted from units of concentration to mass using depth measurements and a correlation between volume and depth of the mesocosms. All values are reported as the mean and the standard error (±) of the mean unless otherwise noted. SAS JMP® Pro 13.0.0 (SAS Institute Inc. Cary, NC, USA) was used to perform statistical analyses. Statistical analyses were used to determine whether treatments differed from each other or the control in terms of N or P removals or differed in other physiochemical properties. Normality assumptions were tested both visually using the histogram and residuals and using the Shapiro-Wilk goodness-of-fit test, and suggested guidelines for skew and kurtosis were compared.
Normally distributed data were analyzed using analysis of variance (ANOVA). Treatment differences were identified using the ANOVA F ratio for data with equal variance; Welch's ANOVA F ratio was used to determine treatment differences for data with unequal variance. The Student t test was used for pairwise comparisons, and Tukey's honestly significant difference (HSD) test was used for multiple comparisons (p < 0.05). When data were non-normal, the nonparametric Wilcoxon/Kruskal-Wallis tests (rank sums) were used treatment comparison (p < 0.05).
Normally distributed data were analyzed using analysis of variance (ANOVA). Treatment differences were identified using the ANOVA F ratio for data with equal variance; Welch's ANOVA F ratio was used to determine treatment differences for data with unequal variance. The Student t test was used for pairwise comparisons, and Tukey's honestly significant difference (HSD) test was used for multiple comparisons (p < 0.05). When data were non-normal, the nonparametric Wilcoxon/Kruskal-Wallis tests (rank sums) were used treatment comparison (p < 0.05).
4
Dexamethasone Depot Effects Analysis
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Data are presented as means ± SEM for the number of independent studies indicated in figure legends. One- or two-way repeated-measures (RM)-MANOVA was used. When main effects or interaction of Dex and depot were significant, post hoc comparisons were made by Dunnett or t tests as indicated in figure legends (GraphPad Prism or SAS JMP Pro 13). P values ≤0.05 were considered statistically significant.
5
Geriatric Interfacility Trauma Triage Score
6
Evaluating Nitrogen Dynamics in Rainfall Events
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The SAS JMP Pro 13 software was used for statistical analysis in this study. Differences in the mean for response variables (i.e., TN, DON, PON, NH4–N, NOx–N, δ15N–NO3–, and δ18O–NO3–) and rainfall variables (i.e., antecedent dry period, amount, intensity and duration of rainfall), were input into a Pearson correlation to test for relationships among the variables. An alpha value equal to 0.05 was used as a threshold for statistical significance.
7
Assessing Burnout and Associated Factors
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Data were analyzed using SAS 9.4 and SAS JMP Pro 13 software (Cary, NC). An internal consistency of the Mini Z was assessed using Cronbach's a coefficient; values 0.8 or greater indicated good scale reliability. Depending on the distributions and a context, the items were either recoded as categorical variables or used to create a continuous scale. In the latter case, raw scores from selected items were summed and converted into a 100-point score. Exploratory factor analysis was based on a principal component factoring method. 16 Continuous variables were analyzed using 2-sided t test; categorical variables were analyzed using 2-sided Fisher's exact test. Association between presence of burnout and burnout drivers was assessed using a generalized estimating equation regression model for ordinal outcome, which accounted for the correlation of the responses within the same practice. Level of significance was set at 2-sided a = 0.05.
8
Stress Impacts on Musculoskeletal Pain
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Analysis was done via SAS JMP Pro 13. Two-tailed P-values were reported with a 95% confidence level (P < .05). Parametric or non-parametric statistics were applied based on the assumptions: sample size, normality (QQ-plot, Shapiro-Wilk) and equivalence of variance (Brown-Forsythe).
Based on the assumptions (linearity, equal variances and normal distribution) correlations between variables were estimated. A logistic regression model was built to calculate associations between stress and the PPTs of the (1) m. temporal and (2) m. anterior tibial.
Spearman"s rho was used to calculate possible associations between the ROM of the TMJ and headache characteristics from the headache diary. Power analysis was based on previous data indicating the standard deviation and mean of "jaw pain", "face pain" and "head pain" measured with the Numeric Rating Scale (0-10). 42 Nineteen participants per group were needed to obtain a power of 80%. Effect sizes (ES) were calculated based on the parametric (Cohen"s d) or non-parametric (r-value, odds ratio (OR)) statistics (Table 2).
Based on the assumptions (linearity, equal variances and normal distribution) correlations between variables were estimated. A logistic regression model was built to calculate associations between stress and the PPTs of the (1) m. temporal and (2) m. anterior tibial.
Spearman"s rho was used to calculate possible associations between the ROM of the TMJ and headache characteristics from the headache diary. Power analysis was based on previous data indicating the standard deviation and mean of "jaw pain", "face pain" and "head pain" measured with the Numeric Rating Scale (0-10). 42 Nineteen participants per group were needed to obtain a power of 80%. Effect sizes (ES) were calculated based on the parametric (Cohen"s d) or non-parametric (r-value, odds ratio (OR)) statistics (Table 2).
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