The reductions of pathogenic count (log CFU/mL) were considered for further statistical analysis to assess the differences between the effects of the tested solutions. The data (means ± standard deviation) were subjected to analysis of variance (ANOVA). Tukey’s multiple range tests were used to determine the significant difference at p < 0.05 using the SPSS statistical package v. 21.0 (SPSS Inc., Chicago, IL, USA).
Spss statistical package v 21
Manufactured by IBM
Sourced in United States
SPSS statistical package v. 21.0 is a software application for statistical analysis. It provides tools for data manipulation, visualization, and advanced statistical modeling. The software is primarily used for analyzing and interpreting data.
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Lab products found in correlation
8 protocols using spss statistical package v 21
1
Pathogenic Count Reduction Analysis
2
Statistical Analysis of Experimental Parameters
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Statistical analysis was performed using the SPSS statistical package v.21.0 (SPSS Inc., Chicago IL). Analysis of variance (ANOVA) for repeated measures was used to evaluate the changes in the parameters over the course of the study and between-groups comparison. The Bonferroni test was used to adjust multiple comparisons. Data non normally distributed were analyzed using Kruskas-Wallis test to compare among groups differences. Values of P < 0.05 were considered significant
3
Nonparametric Statistical Analysis of Data
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When the distribution of variables was non-normal, variables were described using the median and 25th and 75th percentiles. Differences between medians were established using the Mann–Whitney or the Kruskal–Wallis test when appropriate. Correlations were calculated with Spearman rank correlation test. Statistical analysis was performed with the SPSS statistical package v21.0 (SPSS Inc, Chicago, IL).
4
Cadmium Toxicity Amelioration by EDTA and IAA
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The experiments were repeated three times, and the data obtained from the factorial experiments were grouped into cadmium, cadmium/EDTA, cadmium/IAA, and cadmium/EDTA/IAA treatment conditions. An analysis of variance and Duncan’s multiple range test were performed using SPSS Statistical Package v. 21 (IBM, Armonk, NY, USA) to determine the significance at p ≤ 0.05.
5
Genetic Analysis of Stress Tolerance Traits
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Statistical analysis was carried out using the SAS statistics package general linear model (GLM) procedure (SAS Institute Inc 1990 ). The frequency distribution was assessed to test the trait skewness among the RILs. The broad sense heritability (H) was calculated from the covariance values using the formula, H = σ2G/(σ2G+ σ2e/k), where σ2G and σ2e are the genetic and residual variances, respectively, and ‘k’ is the number of replications. The required variance components for calculating heritability were obtained as explained by Fehr (1987 ). The relationship between grain yield under stress and secondary traits was analyzed using linear regression (SPSS statistical package v.21, IBM Corp. Released 2012 ) considering yield under stress as the fixed effect.
6
Postoperative Complications: Risk Factors
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Data were analysed using SPSS ™ statistical package v. 21 (IBMSPSS, NY,USA). Quantitative data were expressed as median and inter-quartile range (IQR). Qualitative data were expressed as frequency and percentage.
The following tests were used:
The following tests were used:
Mann–Whitney test of significance was used when comparing in quantitative data
Chi-square test of significance was used to compare in qualitative data.
7
Dose-Response Curve Analysis of Nicotine Effects
8
Statistical Analysis of Study Groups
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Statistical analysis was conducted using SPSS™ statistical package v. 21 (IBM SPSS, NY, USA). Numerical data were compared using an independent sample t-test, while categorical data were compared using the chi-square test. p-values < 0.05 were considered statistically significant. The epidemiologic and characteristic differences between the two study groups were analyzed using chi square.
An a priori power analysis was conducted using G*Power version 3.1.9.7 to determine the minimum sample size required to test the study hypothesis. Results indicated the required sample size to achieve 80% power for detecting a medium effect, at a significance criterion of α = 0.05, was N = 50 for using independent t test and chi square to test the hypothesis.
An a priori power analysis was conducted using G*Power version 3.1.9.7 to determine the minimum sample size required to test the study hypothesis. Results indicated the required sample size to achieve 80% power for detecting a medium effect, at a significance criterion of α = 0.05, was N = 50 for using independent t test and chi square to test the hypothesis.
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