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Statistical package for social science software version 17

Manufactured by IBM
Sourced in United States

The Statistical Package for Social Science (SPSS) software, version 17.0, is a comprehensive data analysis tool designed for researchers and analysts working in the social sciences. The software provides a wide range of statistical analysis functions, including data management, descriptive statistics, bivariate statistics, and advanced modeling techniques. SPSS 17.0 is a powerful software package that allows users to analyze and interpret complex data sets.

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10 protocols using statistical package for social science software version 17

1

Correlation Between fMRI and SPECT in ADHD

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The data were analyzed using Statistical Package for Social Science software version 17 (SPSS Inc., Chicago, IL). Pearson's correlation was used to examine the relationships between the parametric estimates from fMRI analysis and DAT availability assessed by SPECT. A supplemental generalized linear model analysis was conducted to probe the group differences in the correlation coefficients. The threshold for statistical significance was set at P < .05.
The power for detecting small (r = 0.10), medium (r = 0.30), and large (r = 0.50) effect sizes with our sample size in the group of adults with ADHD was 0.06, 0.19, and 0.51, respectively.
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2

Statistical Analysis of Experimental Data

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The data were analyzed using Statistical Package for Social Science software version 17 (SPSS Inc., Chicago, IL, USA) and expressed as the mean and standard deviation. One-way ANOVA followed by post-hoc Tukey was used to compare groups. p-values of ≤ 0.05 were considered significant.
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3

Effects of Fasting on Heart Rate Variability

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Data normality was tested and confirmed by the Kolmogorov-Smirnov test, allowing the description of values as mean (standard deviation). Mauchly's test confirmed the sphericity of data, enabling the use of parametric statistics for comparisons. Thus, responses during exercise and recovery periods were compared between the FED and FST conditions using a mixed model for repeated measurements. Sidaks's test was applied to determine the differences between conditions and time points. Considering our mixed sample and the range of participants' age, the gender and the total years were considered as covariates in these analyses. The sample size of at least 10 in each group presented an 80% power to detect a difference between HF power means of 1 ms with a level of 0.05 (two-tailed), based on an SD of 0.78 ms obtained from time HRV recordings in a previous cohort of healthy female subjects [17 (link)]. The Statistical Package for Social Science software, version 17.0 (SPSS Inc., Chicago, Illinois) was used and the level of significance was set at p-value <0.05.
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4

Gliding Resistance of Flexor Tendons

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Statistical analyses were performed using the Statistical Package for Social Science software version 17.0 (SPSS, Chicago (IL)). To evaluate the contribution of SSCT to gliding resistance at different excursion velocities, a two-way ANOVA was performed on the force and energy at 100% excursion with factors of SSCT condition (intact and divided) and velocity (2 mm/s and 60 mm/s). When analyzing the gliding resistance of the FDS3 tendon dissociated from the SSCT the paired-t test was used. P values <.05 were considered significant.
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5

Statistical Analysis of Hyperoxemia Outcomes

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Statistics was performed with GraphPad Prism version 6 (GraphPad Software, La Jolla, CA, United States) and Statistical Package for Social Science software, version 17.0 (SPSS Inc., Chicago, IL, United States). Normality of distribution was checked with the Shapiro–Wilk test. Continuous variables were expressed as mean ± standard deviation or median [1st–3rd quartile], as appropriate. Unpaired t-test or Mann Whitney U-test were used for comparisons of two groups. The chi-square test was used for nominal variables. We constructed multivariate binary logistic regression models in order to evaluate the independent association between the exposure to hyperoxemia (prevalence of hyperoxemia, prevalence of hyperoxia + hyperoxemia, daily excess O2) and the outcomes of interest (ICU mortality, diagnosis of VAP). Separate models were constructed for each index of exposure to hyperoxemia in order to avoid multi-collinearity. The basic assumptions for conducting logistic regression analyses were verified, including the absence of multi-collinearity and the linearity of the logit for each continuous independent variable (20 (link)). Covariates included in the models were selected based on their well-established association with the outcome of interest (6 (link), 20 (link)). A p-value < 0.05 was used to indicate statistical significance.
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6

Reliability of CT Image Analysis

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Statistical analyses were performed using the Statistical Package for Social Science software version 17.0 (SPSS Inc., Chicago, Ill). To evaluate the reliability and reproducibility of the CT image analysis results, the identification of landmarks and measurements of parameters were repeated after a 2-week interval in 10 randomly selected images. The intraclass correlation coefficient test (ICC) ranged from 0.92 to 0.98 (intraobserver) and 0.86 to 0.94 (interobserver), indicating a high level of reliability and reproducibility. Comparisons between T0 and T1 were conducted using the paired-samples t-test. A level of P < 0.05 was considered statistically significant.
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7

Categorical Variable Association Analysis

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Pearson's chi-square test was carried out to examine the association between categorical variables, and a value of p < 0.05 was considered significant. All statistical procedures were performed using Statistical Package for Social Science software version 17.0 (SPSS Inc., Chicago, IL, USA).
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8

Normality Analysis of Physiological Data

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Initially, the analysis of data normality was performed and certified by the Shapiro–Wilk test. Asymmetric distribution was observed for VO2peak, normalized by weight (M0 in both AT and CG; P<0.01), vVO2peak (M0 in AT; P=0.04), velocity at GET (M0 in CG; P=0.04), and RMSSD (M0 in CG; P=0.03). Logarithmic transformation did not correct the asymmetry. Thus, all variables were presented as median (interquartile range). Differences between groups at M0 were evidenced by the Student t-test for independent samples (normally distributed variables) and by the Mann–Whitney test for asymmetric variables. Moreover, differences between evaluations (ie, M0 vs M1) were evidenced by the Student t-test for dependent samples (normally distributed variables) and by the Wilcoxon test for asymmetric variables. The Statistical Package for Social Science software, version 17.0 (SPSS Inc, Chicago, IL, USA) was used, and the level of significance was P<0.05.
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9

Statistical Analysis Methodology for Clinical Outcomes

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All statistical analyses were performed with the use of Statistical Package for Social Science software, version 17.0 (SPSS Inc., IBM, Armonk, New York). Continuous variables were checked with normality test (Kolmogorov-Smirnov test). Normal data was presented as mean ± SD. For non-normally distributed parameters, nonparametric test was used as appropriate. The two groups' continuous variables were compared with two-tailed Student's t test or Wilcoxon rank sum test (Mann-Whitney U test). Categorical variables were presented as percentages and were compared using the Chi-square test or Fisher's exact test. Cox proportional hazard model analysis was conducted to estimate the variables' hazard ratio (HR) and its 95% confidence interval (CI) for clinical endpoints. Multivariate Cox proportional hazard model was used to adjust baseline risk factors and to find predictors of clinical events over long term follow-up. Variables with a P value < 0.10 were candidates for multivariate regression model. A 2-tailed P value < 0.05 was considered statistically significant.
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10

Anaerobic Performance in Endurance Sports

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The normality of data was previously confirmed using the Shapiro-Wilk's test, allowing presentation of data as mean ± standard deviation (SD). Mauchly's sphericity test was also applied, allowing the use of parametric statistics. The anaerobic parameters in different performances were compared through analysis of variance (ANOVA) for repeated measurements, followed by Tukey's post-hoc test. Pearson's correlation was used to test the relations between anaerobic parameters and performances in different distances. The correlation coefficient was classified as very small (0.00–0.19), small (0.20–0.39), moderate (0.40–0.69), strong (0.70–0.89), and very strong (0.90–1.00). For all analysis, the Statistical Package for Social Science software, version 17.0 (SPSS Inc, Chicago, Illinois) was used and the level of significance was p < 0.05.
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