Jmp software package

Summary statistics of categorical patient characteristics and the details of the prescribed medicines at discharge were described as numbers and percentages, and continuous variables were described as medians and interquartile ranges (IQRs). Bi-variable analyses were employed to compare the 30-day readmission and no readmission groups for each candidate predictor using the chi-square test for categorical variables and the Wilcoxon rank-sum test for continuous variables. Multivariable logistic regression analysis after controlling simultaneously for each variable was performed to determine the factors associated with 30-day readmission of patients with HF. In multivariable regression analysis, an important assumption is that explanatory variables are independent of each other. Therefore, we used variance inflation factors (VIFs) to test for multicollinearity among the predictor variables. A VIF exceeding 10 was regarded as indicating serious multicollinearity, and a value greater than 4.0 was considered a cause for concern [21 (link)]. P-values, adjusted odds ratios (ORs), and corresponding two-sided 95 % confidence intervals (CIs) were obtained for the predictors. The statistical analyses were performed using release 11 of the JMP software package (SAS Institute Inc., Cary, NC, USA). P-values < 0.05 were considered to represent statistical significance.

The t-test was carried out between the two samples (Mixed Citrus and Lemon), concerning the aroma composition (chemical compounds and classes) and the sensorial properties (colour, aroma, taste, flavour, texture, spreadability, and overall acceptability), while ANOVA test was carried out among all the samples for TPC and antioxidant assays. p < 0.05 was used to assess the significance of differences between means. The statistical analyses were performed using the JMP software package (SAS Institute, Cary, NC, USA).

Comparisons of dichotomous variables between groups were performed using the χ²‐test. Post‐recurrence survival was measured from the date of initial recurrence to the date of death from any cause or the date on which the patient was last known to be alive. Survival probability was estimated using the Kaplan–Meier method. Differences in survival were evaluated using log‐rank tests. Univariate and multivariate analyses associated with PRS were tested using a Cox proportional hazards regression model. Analyses were performed using the JMP software package (version 11; SAS Institute Inc., USA). p‐values of <0.05 were considered to indicate statistical significance.

In this study, for simplicity, the mobility at the transit stations was considered as a surrogate to represent COVID-19 transmission to avoid nonlinear regression computations. This is based on our previous study results that revealed the mobility at the transit stations (Google mobility) as the most important factor characterizing the new DPC [29 (link)]. When using machine learning, the accuracy of the 2-week new DPC forecasting is > 82.6%, whereas the remaining factors included the weather and condition of state-of-emergency [20 ]. Thus, a time window averaging of the mobility was investigated, which is approximately characterized by the incubation time and latency from sample collection to reporting in healthcare facilities to relate with the ERN [20 ]. The mobility at transit stations was averaged over time windows (days) considering the latency (days) (e.g., setting the duration to 6 days and latency to 4 days means averaging the mobility of 4–9 days before the relevant date).
The correlation between the ERN and public mobility with latency was analyzed using the Pearson and Spearman rank correlation. The JMP software package (SAS Institute, Cary, NC, USA) was used for statistical analysis. A p-value of < 0.05 was considered statistically significant to specify the dominant factors that influence the rates.

The chemical evaluations were performed in triplicate and data are reported as mean values. Statistical analysis of compositional data was performed by one-way ANOVA (CoStat, Cohort 6.0), and means separation by the Tukey’s HSD test at p ≤ 0.05 of significance.
Statistical analysis of volatile organic compounds characterization was performed by means of the JMP software package (SAS Institute, Charlotte, NC, USA). In particular, hierarchical cluster analysis (HCA) was carried out using Ward’s method [55 (link)], with squared Euclidian distances as a measure of similarity on unscaled data. The data matrix was constituted by the complete volatile profiles.
Sensory analysis results were processed by Big Sensory Soft 2.0 (version 2018). In particular, sensory data were analyzed by two-way ANOVA with panelists and samples as main factors [53 ].
Partial least squares regression (PLS regression) was applied to sensory data in order to define the correlation among quantitative and hedonic parameters, using XLSTAT version 2019.4.1 (Addinsoft Inc. 244 Fifth Avenue, Suite E100, New York, NY, USA, 10001).

Statistical analyses were performed using JMP software package (SAS, Tokyo, Japan). To compare the distribution of TIL density according to the state of lymph node metastasis, we performed Student’s t test. Pearson’s chi-square test was used to evaluate the correlation between two groups based on clinicopathological features. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using logistic regression analysis. Multivariable analysis was performed using the multivariable logistic regression model. P-values less than 0.05 were considered significant.

Statistical analysis was conducted using the JMP software package (SAS, Tokyo, Japan). The relationship between each factor was examined using the Chi squared test (or Fisher’s exact test when necessary). A p value < 0.05 was considered significant.