Pearson correlation coefficients were used to determine the linear correlation between pairs of growth-related traits, using software SPSS version 16.0 (SPSS Inc., USA). The mean size of growth-related traits was compared in the large and small subpopulation using the Student’s t-test in SPSS version 16.0. The significance level was set to 0.05. The QTL mapping was performed using MapQTL 5 software [25] and the KW non-parametric test was performed to determine the significant relationship between the regions of the genome and growth-related traits. We used the nonparametric KW analysis because the growth-related traits of the individuals used in this study deviated from a normal distribution, and no assumptions were being made about the probability distributions of the quantitative traits. The KW test ranks all individuals in accordance with the quantitative trait and classifies them in accordance with their marker genotype. A KW value larger than the thresholds given by the KW test (χ2 test, P<0.005) and a degree of correlation between loci and traits that was equal to or greater than four asterisks were used to identify QTL-peak loci [25] . The genotype frequency difference between the large subpopulation and the small subpopulation was compared using the Chi-square test in SPSS version 16.0. The significance level was set to 0.05.
Spss version 16
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SPSS version 16.0 is a software application used for statistical analysis and data management. It provides tools for data entry, data manipulation, and the generation of statistical reports and graphics.
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Lab products found in correlation
Prism 5, by GraphPad (37 mentions)
Epidata version 3, by IBM (15 mentions)
Prism 6, by GraphPad (12 mentions)
Spss for windows version 16, by IBM (9 mentions)
Sas version 9, by SAS Institute (8 mentions)
Prism 8, by GraphPad (7 mentions)
Prism software, by GraphPad (7 mentions)
Epi info version 3, by IBM (7 mentions)
Excel 2010, by IBM (5 mentions)
Prism version 6, by GraphPad (4 mentions)
Stata version 11, by StataCorp (4 mentions)
Origin 8, by OriginLab (4 mentions)
Prism version 5, by GraphPad (3 mentions)
Office excel 2007, by IBM (3 mentions)
Stata version 10, by StataCorp (3 mentions)
R version 3, by R Project for Statistical Computing (3 mentions)
Excel 2013, by IBM (3 mentions)
Spss ver 16, by IBM (3 mentions)
Originpro 8, by OriginLab (3 mentions)
Prizm version 7, by GraphPad (3 mentions)
3 842 protocols using spss version 16
1
QTL Mapping of Growth Traits
2
Analyzing Cellular and Antibody Responses
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CFU data were normalized by log transformation and evaluated by one-way analysis of variance, followed by Dunnett's post-hoc test using GraphPad software (GraphPad software, Inc., La Jolla, CA, USA). The cellular responses were compared between the groups using one-way analysis of variance and Tukey's Multiple Comparisons test. The analyses were performed with SPSS version 16.0 software (SPSS, Chicago, IL, USA). The antibody responses were compared between the groups using two-way matching by rows (RM) analysis of variance and matching by rows test. The analyses were performed with SPSS version 16.0 software (SPSS, Chicago, IL, USA).
3
Genetic Polymorphisms and Congenital Heart Defects
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Chi-square statistics tested the differences in covariates between the cases and controls. Unconditional logistic regression analysis was performed to investigate the association between maternal exposure to PAHs and foetal CHDs using Statistical Package for Social Sciences (SPSS) version 16.0 software (SPSS Inc., IBM, Chicago, USA).
Hardy–Weinberg equilibrium was assessed in the controls using Plink software (http://pngu.mgh.harvard.edu/~purcell/plink/ ). The pairwise linkage disequilibrium (LD) patterns and haplotype structures of CYP1A1, CYP1A2, CYP1B1, CYP2E1 and AHR genes were analysed using Haploview 4.2 software. Unconditional logistic regression analysis was performed to investigate the association between individual genetic polymorphisms and CHDs using Plink software.
The effects of the gene-exposure interactions on CHDs occurrence were evaluated by logistic models using SPSS version 16.0 software (SPSS Inc., IBM, Chicago, USA).
All analyses were adjusted for covariates/potential confounders. False discovery rate (FDR) correction of multiple hypothesis testing was performed. Two-sided P < 0.05 was considered statistically significant.
Hardy–Weinberg equilibrium was assessed in the controls using Plink software (
The effects of the gene-exposure interactions on CHDs occurrence were evaluated by logistic models using SPSS version 16.0 software (SPSS Inc., IBM, Chicago, USA).
All analyses were adjusted for covariates/potential confounders. False discovery rate (FDR) correction of multiple hypothesis testing was performed. Two-sided P < 0.05 was considered statistically significant.
4
Statistical Analysis of Experimental Data
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Statistics were calculated with OriginPro version 8.0 software (OriginLab Corporation, Northampton, MA, United States). The data are presented as the mean ± SD or mean ± SEM. Differences between groups were determined by one-way ANOVA analysis by SPSS version 16.0 software (SPSS Inc., Chicago, IL, United States). All p values <.05 were considered statistically significant (*p < .05, **p < .01; #p < .05, ##p < .01; Δp<.05, ΔΔp<.01). The OriginPro version 8.0 software and SPSS version 16.0 software were obtained from the Library of Yunnan University.
5
Effect of Household Processing on Vegetable Nutrition
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Descriptive statistics such as means and standard deviation were calculated using SPSS version 16 software. One-way analysis of variance (ANOVA) was used to see the effect of household processing methods on mineral contents and the bioavailability of minerals in selected vegetables. Multiple comparison tests using least significant difference technique (LSD, P < 0.05) were applied to compare the means of each parameter between different household preparation practices using SPSS version 16 software. Paired comparison t-test was used to determine if there was a significant mean difference between raw and processed vegetables for each parameter.
6
Statistical Analysis of Experimental Data
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Statistical analysis of the present study was conducted, using the mean, standard error, and t-tests by the Statistical Package for the Social Sciences (SPSS) version 16 (SPSS Inc., Chicago, IL). The normality of data was measured using Shapiro-Wilk test by SPSS version 16.[33 34 ] In all cases, a P value of <0.05 was considered to be statistically significant.
7
Statistical Analyses for Normality and Group Comparisons
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Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS), version 16.0 for Windows (SPSS Inc., Chicago, IL, USA). Continuous variables were analyzed for normality of the distribution using the Kolmogorov-Smirnov test (One-Sample Kolmogorov-Smirnov D-Test in SPSS, version 16; SPSS Inc.). When the level of signif-icance in this test was lower than 0.05 (p <0.05), the hypothesis for normal distribution was rejected. The continuous variables with normal distribution were com-pared between two or more independent groups by the Student t-test or one-way analysis of variance (ANOVA) test with least significant difference (LSD) post hoc ana-lysis, while those with an abnormal distribution were analyzed with the Mann-Whitney U or Kruskal-Wallis tests. The frequencies of distribution in the contingency tables were analyzed using χ2 test, or Fisher’s exact test, when needed. The odds ratio (OR) and 95% confidence interval (95% CI) were calculated by binary logistic regression with age and sex as covariates. The Hardy-Weinberg equilibrium (HWE) was calculated by an inter-active calculation tool for χ2 tests of goodness of fit and independence [24 ]. Factors with a p value of <0.05 were considered to be statistically significant.
8
Dietary Quality Index and Abdominal Obesity
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All data were analyzed using the statistical software package SPSS version 16 (SPSS version 16; SPSS Inc). We considered P < 0.05 to represent statistical significance level. In the CQI of the adjusted energy diet, participants were divided into quintiles. In CQI quintiles, quantitative variables are reported as mean and standard deviation and qualitative variables as frequency (percent) as tables. Chi-square test was used to compare the frequency of qualitative variables among the CQI quintiles and Analysis of Variance (ANOVA) was used to compare the means of quantitative variables. Also, nutritionist 4 software, adapted to Iranian foods, was used to analyze nutrients intake. To diagnose abdominal obesity in participants, Odds Ratios (OR) and Confidence Intervals (CI) were determined through binary logistic regression in two target models; unadjusted, and adjusted for BMI, physical activity, age, sex, energy, education, and marital status.
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All data were analyzed using the statistical software package SPSS version 16 (SPSS version 16; SPSS Inc). We considered P < 0.05 to represent statistical significance level. In the CQI of the adjusted energy diet, participants were divided into quintiles. In CQI quintiles, quantitative variables are reported as mean and standard deviation and qualitative variables as frequency (percent) as tables. Chi-square test was used to compare the frequency of qualitative variables among the CQI quintiles and Analysis of Variance (ANOVA) was used to compare the means of quantitative variables. Also, nutritionist 4 software, adapted to Iranian foods, was used to analyze nutrients intake. To diagnose abdominal obesity in participants, Odds Ratios (OR) and Confidence Intervals (CI) were determined through binary logistic regression in two target models; unadjusted, and adjusted for BMI, physical activity, age, sex, energy, education, and marital status.
9
Comparative Analysis of Phytochemical Profiles
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Statistical significance was determined using one-way analysis of variance (ANOVA) followed by a post hoc test (Duncan's multiple range or Tukey's multiple comparison tests). Data on total phenolic content, total flavonoid content, β-carotene, ascorbic acid and DPPH free radical scavenging activity were subjected to ANOVA followed by Tukey's post hoc test using GraphPad Prism version 5.02 (GraphPad Software Inc., San Diego, USA). SPSS version 16 (SPSS Inc., Chicago, IL, USA) was used to evaluate significant differences in the concentrations of phenolic acids. Differences in phenolic acid concentrations were further separated using Duncan's multiple range test. All analyses were done at a probability of α = 0.05. Normality of residuals and equality of variance were tested using the Kolmogorov-Smirnov and Levene's tests (SPSS version 16). Percentage data were arcsin transformed prior to being subjected to ANOVA.
10
Dietary AVP Effects on Weight and FCR
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The data were statistically investigated using a one-way analysis of variance (ANOVA) (SPSS version 16.0, SPSS Inc., Chicago, IL, USA). Tukey’s multiple comparisons post hoc test was used to compare the differences between groups, with statistical significance set at p < 0.05. The results of the analysis are presented as means ± SE (standard error). In addition, a fit regression model was created between the increasing dietary levels of AVP, and weight gain and FCR were estimated using SPSS version 16, SPSS Inc., Chicago, IL, USA.
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