Spss statistics 17.0 for windows

Data were expressed as mean ± standard deviation (SD) or median [interquartile range]. Continuous variables were compared using the Jonckheere-Terpstra trend test. Chi-square test was used to compare categorical variables.
Odds ratio (OR [95% confidence intervals (CI)]) was calculated by logistic regression analysis using unadjusted and multivariate adjusted models in each sex. Model 1 was adjusted for age. Model 2 was adjusted for variables in model 1, plus time on HD and presence of diabetes mellitus (DM) as a primary cause of end-stage renal disease (ESRD). Model 3 was adjusted for variables in model 2, plus serum Cr, albumin, phosphorous, C-reactive protein (CRP) and dialysis dose (Kt/V_urea) assessed by the single-pooled urea kinetic model. Percentile creatinine generation rate (%CGR) [21 (link)] and normalized protein catabolic rate (nPCR) [22 (link)] were also calculated from Shinzato’s formula.
All calculations were performed using SPSS statistics 17.0 for Windows (SPSS Inc., CA, USA).

Statistical analysis was performed using SPSS Statistics 17.0 (SPSS Inc.,). A one‐way analysis of variance was performed to determine the overall effect of different treatments, and Duncan's test was used for multiple comparisons; the significance level was set at p < .05. To establish a correlation between the phytochemical profile and antioxidant activities, bivariate Pearson's correlation analysis was performed using a two‐tailed test with IBM SPSS Statistics 17.0 for Windows (SPSS Inc.,) and multivariate correlation was conducted by partial least squares regression (PLS) using Unscrambler 10.1 (Camo Process AS). In the PLS method, the predictors (variable X) were the content of the phytochemical profile, with the responses (variable Y) being the PSC and CAA values.

All experiments were conducted in triplicate. The mean and standard error of the mean were determined using SPSS Statistics 17.0 for Windows (SPSS Inc., Chicago, IL, USA). Differences among the means of treatment were determined using analysis of variance (ANOVA) with Duncan’s multiple range post hoc analysis at the 95% confidence level. Principal component analysis (PCA) and biplots were also generated to analyse the correlation structure of a group of multivariate observations and provide the axis along which the maximum variability in the data occurred.

Biomechanical parameters (mean values) were analyzed with the paired t-test to determine differences between towing modes A group and B. The 95% confidence interval (CI) of the population mean of the shifting distance, and correlation coefficient between traction stiffness (TS) and age were calculated.
As for the clinical analysis of the treatment diary, the chi-square test was used to determine differences of female-to-male ratio between A → B and B → A. Other data such as age, height, weight, and body mass index were analyzed using the t-test for equality of means between A → B and B → A. The Mantel-Haenszel chi-square for 2 x r tables was used to evaluate the results of the self-report assessments for each traction mode. All statistical analyses were performed with SPSS Statistics 17.0 for Windows (SPSS Inc.).

Data analysis was performed using SPSS Statistics 17.0 for Windows. The observed frequencies were compared with the expected frequencies and tested for the Hardy–Weinberg equilibrium. The expected results are presented with 95% confidence intervals (CI). The odds ratio (OR) for the genotypes and the alleles was calculated. Then, the effect of the UGT1A1 genetic variants on T-score, Z-score, L2L4AM (bone mineral density compared with an age-matched), L2L4YA (bone mineral density in young adult), L2L4BMD (bone mineral density between lumbar vertebrae L2–L4), BMI (body mass index), and other clinical parameters was evaluated. Correlation analysis between the genotypes and the clinical parameters was conducted using one-way ANOVA. The p-value of < 0.05 was considered as statistically significant.
All methods were carried out in accordance with relevant guidelines and regulations.

Descriptive statistical analysis and correlation analyses were performed using the statistical software package SPSS Statistics 17.0 for Windows (SPSS, Inc., Chicago, IL, USA). Two-tailed ANOVA was used for comparisons between blocks and genotypes. The broad-sense heritability (H²) for RCA gene expression or grain yield in each growing season was implemented by SAS 9.2 (SAS institute, Cray, USA) and was estimated by the following equation H² = V_g/(V_g + V_e/r), where V_g and V_e represent genetic variance and error variance, respectively, and were calculated from the ANOVA as described above; r is the block replicate number in field experiment. Frequency distribution analysis was performed using GraphPad Prism (version 7, GraphPad Software, San Diego, CA).

All the statistical analyses for this study were performed using SPSS Statistics 17.0 for Windows. The Chi-square test was used to calculate that the genotype prevalence and allele frequencies were in the Hardy–Weinberg equilibrium. We evaluated the effect of the CYP19A1 polymorphism on selected biochemical and clinical parameters. Analysis for data distribution was performed using one-way analysis of variance (ANOVA). Values normally distributed were expressed as means ± SEM (standard error of mean).