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Pasw statistics version 20

Manufactured by IBM
Sourced in United States

PASW Statistics version 20.0 is a software application designed for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and reporting. The core function of PASW Statistics is to enable users to perform a wide range of statistical procedures, from basic descriptive statistics to advanced modeling and forecasting.

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30 protocols using pasw statistics version 20

1

Evaluating Glycemic Response of Gummy Candies

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Statistical analysis was conducted using PASW® Statistics Version 20.0 (SPSS, IBM®, Armonk, NY, USA). Descriptive statistics (mean ± standard deviation (SD)) were determined for all of the variables. All the subject acceptability scores were compared using a paired t-test (normally distributed) or Wilcoxon test (abnormally distributed). The area under the glucose response curve (AUC) above the fasting blood glucose levels was calculated using the Prism 8 software (GraphPad, San Diego, CA, USA). GI values of the sample food were calculated by averaging the tested food’s AUC over the AUC of glucose then multiplied by 100. The GL per gummy was calculated as the product of the GI and the weight in grams of the available carbohydrates in a gummy then divided by 100. The generalized estimation equation (GEE) was used to compare the effects of different fudge and time points on blood glucose concentration. Significant differences were determined at p < 0.05.
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2

Comparative Analysis of Subject Groups

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Statistical analysis was conducted using PASW Statistics Version 20.0 (SPSS, IBM, Armonk, NY, USA). Descriptive statistics (mean (+/−SD, SEM) and prevalence data where appropriate) were determined for all variables. Characteristics of subjects in the three groups were compared using either one-factor analysis of a variance (ANOVA) followed by Tukey’s test (parametric data) for continuous variables or a chi-square test for categorical variables. A P value of <0.05 was considered statistically significant.
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3

Statistical Analysis of Experimental Data

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PASW Statistics version 20.0 (SPSS, IBM Corp., Armonk, NY, USA) was used for statistical analysis. All data are presented as the mean ± standard deviation. A paired t‐test was carried out to determine significant differences between the two experiments. The level of statistical significance was set at 0.05.
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4

Statistical Analysis of Cost Distributions

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Descriptive statistics or frequency distributions such as mean, standard deviation, median, and percentage were presented for continuous or categorical variables. A χ2 test or Fisher exact test was used for categorical variables. For comparisons of cost according to disease status, as the values did not have a normal distribution, an analysis of variance was used after converting the values to a log scale and the Mann–Whitney test or Kruskal–Wallis test was additionally used. All statistical analyses were 2-sided and performed using PASW Statistics version 20.0 (SPSS Inc, Chicago, IL). P < 0.05 was considered to be statistically significant.
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5

Determinants of Cord Ferritin Levels

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Statistical analysis was conducted using PASW® Statistics Version 20.0 (SPSS, IBM® U.S.A.). The distribution of all variables was tested by Kolmogorov–Smirnov. Descriptive statistics (mean and SD; median and interquartile range; frequency and percentage) were determined for all variables. Primary correlations were explored using Spearman’s test (non-parametric). A P-value <0.05 was considered statistically significant.
Determinants of cord ferritin levels including gestational age, birth weight, maternal BMI at delivery and paternal smoking status were explored in unadjusted models. The residuals of the multiple linear regression model were slightly deviated from normal distribution; therefore a square-root transformation of cord ferritin levels, birth weight, length, and gestational age was used, the residuals were approximately normally distributed when the regression model was repeated. All tests were two-sided at the 5% statistical significance level, P<0.05 being considered statistically significant.
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6

Predictors of C/T Therapy Failure

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The primary end point was to identify predictors of clinical failure of C/T therapy.
Continuous variables were compared using the Student t test and Mann-Whitney U test for normally and non–normally distributed variables, respectively. The χ 2 test or Fisher exact test was used to compare categorical variables. All pretreatment variables identified during univariate analysis were tested using logistic regression analysis to identify risk factors associated with clinical failure. In a multivariate analysis, the model was tested using a backward stepwise selection and P < .05 for all variables in order to determine the effects of all anamnestic, clinical, and therapeutic variables on clinical success or failure of C/T therapy. Empiric and definitive therapy were adjusted for confounders (definitive and empiric regimens, respectively). All tests of statistical significance were 2-tailed. Differences were considered statistically significant at a P value of <.05. Statistical analysis was performed with the software package PASW Statistics, version 20.0 (SPSS Inc., Chicago, IL, USA).
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7

Exercise Intensity and Metabolic Responses

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Microsoft Office Excel 2017 and PASW Statistics version 20.0 (SPSS, IBM Inc. BM Corp., Armonk, NY, USA) were used for data processing and statistical analyses, respectively. All the variable results are presented as mean ± standard deviation. To examine the main effect (Condition and Exercise intensity) and interaction (condition × exercise intensity), two-way repeated analysis of variance (ANOVA) was conducted for each variable. One-way repeated ANOVA was conducted to examine the main effect for each variable in each condition separately if a significant interaction was observed. A paired t-test was conducted to determine the significant differences between TNP and control experiments. The statistical significance level was set at 0.05.
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8

Gross Energy Expenditure During Body Weight Resistance Exercises

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Microsoft Office Excel 2017 and PASW Statistics version 20.0 (SPSS, IBM Inc., BM Corp., Armonk, NY) were used for data processing and statistical analyses, respectively. All the variable results are presented as mean ± SD and 95% confidence interval (CI). Linear regression analyses were conducted to calculate the slopes and intercepts describing the relationship between gross EE and repetition frequency for each BWRE-slow. Paired t-tests were conducted to compare the slope, intercept, and resting EE for each exercise. In addition, we conducted paired t-tests to compare the mean EEs computed for the repetition frequency of 10 repetitions·min−1 using 2 different calculation methods. The statistical significance level was set at 0.05.
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9

Analyzing Energy Expenditure and STS

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Microsoft Office Excel 2017 and PASW Statistics version 20.0 (SPSS, IBM Inc. BM Corp., Armonk, NY, USA) were used for data processing and statistical analyses, respectively. All the variable results are presented as mean ± standard deviation. The linear regression analyses were performed to calculate the slopes and intercepts for the gross EE against the STS frequencies. A paired t-test was conducted to compare the slope, the intercept, and the REE. The statistical significance level was set at 0.05.
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10

Exploring Exercise-Induced Physiological Responses

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Microsoft Office Excel 2017 and PASW Statistics version 20.0 (SPSS, IBM Inc. BM Corp., Armonk, NY, USA) were used for data processing and statistical analyses, respectively. All variable results are presented as mean ± SD. Simple linear regression analysis was conducted to examine the relationship between the total distance and HR responses, EE. Participants who cycled individually for at least 2 consecutive hours were included in the analysis, this study included 10 male adults (age, 44.9 ± 12.1 years).
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