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Spss statistical software version 22.0 for windows

Manufactured by IBM
Sourced in United States

SPSS version 22.0 for Windows is a statistical software package designed to analyze data. It provides a range of statistical procedures and data management tools for researchers and data analysts.

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Lab products found in correlation

5 protocols using spss statistical software version 22.0 for windows

1

Statistical Analysis of RPL Values

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The IBM SPSS statistical software version 22.0 for Windows was used for statistical analyses. The Shapiro-Wilk test was used to assess normality. All RPL values in both groups conformed to a normal distribution (P > 0.05), while the other variables did not (P < 0.05). The variables that conformed to a normal distribution were presented as mean ± SD and the ones that did not as median (min-max). The Wilcoxon t-test and paired t-test were used for intragroup comparisons, while Student's t-test and the Mann–Whitney U test were used for intergroup comparisons. A P value <0.05 was considered significant.
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2

Genetic Analysis of Dyslipidemia in Obesity

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Epidata 3.02 software (Epidata Association, Odense, Denmark) was used to establish a database, and the double entry method was used for data input and logic error detection. SPSS statistical software version 22.0 for Windows (IBM: Almon, NY, USA) was used for all data analysis. For descriptive statistics, t-test or Wilcoxon rank sum test were performed as appropriate after checking for normality, the chi-square test was used to evaluate differences between groups for the categorical variables. The gene counting method was used to calculate genotype and allele frequencies. The chi-square test was used to test for Hardy-Weinberg equilibrium, the odds ratio (OR) and 95% confidence interval (95% CI) and interactions between ApoA1 gene and types of obesity were assessed by binary logistic regression after controlling for potential confounders (sex, age, blood pressure, high TC, high TG, high LDL-C, smoker, drinker and FPG). P < 0.05 were considered statistically significant, the significance threshold was adjusted for multiple comparison tests according to Bonferroni correction, and set at P < 0.025 (0.05/2 = 0.025) when evaluating associations between genotypes in APOA1 gene and low HDL-C disease.
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3

Evaluating Bronchial Airflow Obstruction

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The detection powers of the EtCO2 test, balloon occlusion test and EtCO2 test plus balloon occlusion test were expressed as percentages. Different EtCO2 values between the affected bronchus and the non-affected bronchus, main bronchus and main carina were expressed as mean (95% confidence interval), and were assessed by independent sample Student’s t test. Statistical analysis was performed using the SPSS statistical software version 22.0 for Windows (SPSS, Inc., Chicago, IL, USA). A p value <0.05 was considered as statistically significant.
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4

Psychometric Validation of Emotional Experiences Scale

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Descriptive measures included means, standard deviations, and absolute and relative frequencies. Principal component analysis (PCA) was used to identify the factors from the EES-C. Bartlett’s test was used to assess whether the correlation between items was adequate. The Kaiser-Meyer-Olkin (KMO) statistic was used to assess sample adequacy. The appropriate number of derived factors were identified using the scree-plot (looking for inflexion points), since the study sample was not large enough to support the Kaiser criterion. Loadings of each item on derived factors were maximized using the orthogonal varimax rotation. Criterion- related validity was assessed by Pearson’s rho correlations with CDI and STAIC-trait anxiety. Cronbach’s alpha values were calculated to assess internal consistency of the identified factors. After, EES-C total score was assessed for meaningful associations with the other measurements of the study. The level of significance p was 0.05. Statistical analyses were performed using the SPSS statistical software version 22.0 for Windows (SPSS Inc., Chicago, IL).
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5

Validity and Reliability Assessment of PRQ

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Descriptive analyses were performed to calculate means, frequencies, and standard deviations (SD). The Kaiser-Meyer-Olkin (KMO) statistic and Barlett’s Sphericity Test were used to examine sample’s adequacy. Principal component analysis (PCA) was performed to identify items’ factors. The appropriate number of factors was identified using the Scree-plot, since the study sample was not large enough to support the Kaiser criterion. The orthogonal varimax rotation was used for loadings of each item on derived factors to be maximized. Criterion-related validity was assessed by Pearson’s rho correlations with SiC and satisfaction measure. Cronbach’s alpha values were calculated to assess internal consistency of the identified factors. The PRQ’s subscales scores were assessed for meaningful associations with the other measurements of the study. The level of significance p value was 0.05. The SPSS statistical software version 22.0 for Windows was used for all statistical analyses (SPSS Inc., Chicago, IL).
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