All analyses were performed using the SPSS statistical software package, version 25.0 (IBM SPSS Inc., Chicago, IL, USA). Descriptive statistics are reported as means ± standard deviations for continuous variables, and percentages and frequencies, for categorical variables. Independent samples t-tests were used to compare continuous variables, and chi-square tests were used to compare categorical variable between males and females. Multiple linear regressions were used to determine the associations between dietary factors and BP in the overall population as well as split by gender. Model 1 (overall population) was adjusted for sex, age, income, total calorie intake, BMI, PASE scores, and BP medication use. Model 2 (split by sex) was adjusted for age, income, total calorie intake, BMI, PASE scores, and BP medication use. The independent variables in the models were normally distributed and did not have any outliers that were of concern. Effect sizes were calculated as f2 (effect size for independent variable) = squared semipartial (part) correlation coefficient for independent variable ÷ 1 − squared multiple correlation coefficient for the full model (R2). The effect sizes were interpreted as follows: f2 < 0.02 small effect, 0.15 medium effect, and 0.35 large effect [14 ]. The significance level was set at p < 0.05.
Spss statistical software package version 25
Manufactured by IBM
Sourced in United States
SPSS Statistics is a software package used for interactive, or batched, statistical analysis. It is capable of handling a wide variety of data formats and can perform both simple and complex statistical analyses.
Automatically generated - may contain errors
Lab products found in correlation
Prism version 8, by GraphPad (1 mentions)
Harmony high content imaging and analysis software, by PerkinElmer (1 mentions)
Prism 6, by GraphPad (1 mentions)
Bs 2081r, by Bioss Antibodies (1 mentions)
Bs 6319r, by Bioss Antibodies (1 mentions)
Metaboanalyst 4, by Sartorius (1 mentions)
Prism 9, by GraphPad (1 mentions)
34 protocols using spss statistical software package version 25
1
Dietary Factors and Blood Pressure
2
Assessing Birth Type and Gender Impact
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Descriptive statistics including mean ± standard and median deviations were calculated. Quantitative variables were compared using Student’s t-test, MANCOVA and MANOVA. A multivariate analysis was performed to assess whether the type of birth or gender influenced the benefits of the intervention. Data normality was confirmed using the Shapiro–Wilk test. A p-value < 0.05 was considered statistically significant. The statistical analysis was performed using SPSS statistical software package version 25.0 (IBM SPSS Inc, Chicago, IL, USA).
3
Statistical Analysis of Treatment Effects
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Data from all treatments were subjected to analysis of variance (ANOVA) by the SPSS statistical software package version 25 (SPSS Inc., USA). Significant differences between treatment means were identified by Student’s t-test and Duncan’s multiple range test at the p < 0.05. Origin (Origin 2019, USA) was used for figure construction.
4
Predictors of BRIEF-A GEC Changes
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Descriptive analyses and linear mixed model analyses were performed using SPSS statistical software package version 25. The threshold for statistical significance was set to p <0.05. Linear mixed model analyses were used to analyse the impact of the predictor variables, one at a time, on pre-treatment level and change scores in the BRIEF-A GEC from pre-treatment to 6-month follow-up. The linear mixed model was specified as a random intercept fixed slope model and the estimator was set to restricted maximum likelihood. Only fixed effects are presented. Missing values were assumed to be missing at random (43 ) and all available information in outcome for participants were included in the analyses. In order to enhance interpretation of the results continuous predictor variables were mean centered. A Bonferroni post hoc test was used to adjust for multiple tests (p < 0.004). In Tables 2 , 3 in the result section are variables that were significant after Bonferroni correction are presented in bold. No co-variates were included in the analyses.
5
MAGE-A6 and MAGE-A11 Expression Analysis
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Statistical analyses were carried out through the SPSS statistical software package version 25 (SPSS, Chicago, IL, USA). The association and correlation between MAGE-A6 or MAGE-A11 expression and clinicopathological characteristics were analyzed using Pearson’s χ2, R tests, and One-way ANOVA. The pairwise comparisons across the groups were performed through Mann–Whitney U test. Survival analysis was estimated through the Kaplan–Meier method and the estimated curves across the groups were compared using the log-rank test. A p-value of < 0.05 was regarded as statistically significant. Charts were drawn through Prism version 8.3.0 software (Graph Pad Inc., San Diego, CA, USA) and SPSS graphs.
6
Evaluation of Antioxidant Activity
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All data and figures were processed using the Office Excel version 2019 software and Origin 2019 software. The SPSS statistical software package Version 25 was used for statistical analysis, and significant differences were identified by one-way analysis of variance (ANOVA). Different letters indicated significantly different values when p < 0.05 or 0.01 (ANOVA).
7
Nutritional Knowledge and Dietary Regimen Impact
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The procedure of absolute frequency tables and percentages was used for statistical analysis of the data. To explore the distribution of sociodemographic variables according to the level of nutritional knowledge and dietary regimen, the Chi-square test was considered. In addition, logistic regression analysis was performed to analyze the association between nutritional knowledge and dietary regimen with excess body weight (overweight/obesity). Crude odds ratios (COR) and 95% confidence intervals (CI) were estimated using simple logistic regression analysis. Variables with a p-value of < 0.25 in the simple logistic regression analysis were included in the final multiple logistic regression model. Multiple logistic regressions were controlled for sex, age, region of origin, marital status, and education level. Finally, the results were reported in adjusted odds ratio (AOR) with a 95% confidence interval. The significance level was set at 0.05. Data processing and analysis were performed with the SPSS statistical software package, version 25 (SPSS Inc., Chicago, IL, USA).
8
Sleep Outcomes in Term and Preterm Children
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All analyses were performed using the SPSS statistical software package version 25 (SPSS Inc., Chicago, IL, USA). Independent samples t-tests and chi-squared tests were used to examine differences in demographic, clinical and sleep variables between children born at term and preterm, and between SGA and non-SGA. Multinomial logistic regression analyses were conducted separately for preterm birth, LBW and SGA to examine the predictive effect of these variables on sleep duration and nocturnal awakenings. Both crude and adjusted models were examined, the latter adjusting for the following covariates entered in one block: gender, parity, maternal age maternal education and breastfeeding. For sensitivity purposes, we additionally adjusted for prematurity when examining the effect of SGA and BW on sleep outcomes. All tests were two-tailed with the significance level set at p<0.05.
9
Neurite Length Quantification Protocol
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The maximum neurite length was measured employing Harmony High Content Imaging and Analysis software (Perkin-Elmer). The results were normalized to the maximum neurite length of control differentiated cells. The data obtained in the immunofluorescence assays were compared by Student’s t-test using GraphPad Prism® 6.0 software (GraphPad, La Jolla, CA, USA), with the exception of data of the effect of the hits from Prestwick Chemical Library, that were compared by ANOVA followed by Dunett’s post-hoc test employing SPSS statistical software package version 25.0 (IBM Corporation, Armonk, NY, USA). The differences were considered statistically significant at p<0.05.
10
Prognostic Biomarkers in Cancer Survival
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Statistical analysis was conducted using SPSS statistical software package version 25.0 (IBM, Armonk, USA) and SigmaPlot version 12.5 for Windows (Systat Software, Inc., San Jose, CA, USA). Continuous data are shown as the mean ± standard deviation (SD) or median with range. Categorical variables are presented as frequencies and percentages and were compared using Pearson’s chi-square test. Receiver operating characteristic (ROC) curve analysis was applied to identify the optical cut-off values of PNI and SII with the highest Youden’s index for predicting 5-year OS. The primary end point was OS, and the secondary end point was CSS. Survival analysis was performed using the Kaplan-Meier method, and survival curves were compared by the Log rank test. A Cox proportional hazards regression model was used to analyse various independent predictors of postoperative OS and CSS. Univariate analysis was used to assess various factors related to patient and tumour characteristics. Multivariate analysis was performed only for variables with p<0.1 in univariate analysis. A two-tailed p<0.05 was considered statistically significant.
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