Sensitivity, specificity, positive likelihood ratio, and accuracy were calculated using standard definitions.[14 ] The relationship between sSUVmax and mSUVmax was analyzed by linear regression analysis using SPSS Statistics version 22 software (IBM Japan Ltd, Tokyo, Japan) and Stata version 14 software (Stata Corp LP, College Station, TX). Receiver operating characteristic (ROC) curve analyses were generated using SigmaPlot version 13 software (Systat Software, Inc, San Jose, CA) and Stata version 14 software. Cronbach alpha values were analyzed for inter- and intraobserver reproducibility by SPSS Statistics version 22 software. A P < .05 was considered to indicate a statistically significant outcome.
Spss statistics version 22
Manufactured by IBM
Sourced in United States, Japan, United Kingdom, Germany, Belgium, Australia, Spain, Switzerland
SPSS Statistics version 22 is a statistical software application developed by IBM. It is designed to analyze and manipulate data, providing users with tools for data management, statistical analysis, and reporting. The software supports a wide range of data types and offers a variety of statistical procedures, enabling users to explore relationships, test hypotheses, and generate insights from their data.
Automatically generated - may contain errors
Lab products found in correlation
Sas version 9, by SAS Institute (26 mentions)
Prism 5, by GraphPad (20 mentions)
Prism 6, by GraphPad (14 mentions)
Prism 8, by GraphPad (14 mentions)
Prism 7, by GraphPad (10 mentions)
Sas 9, by SAS Institute (7 mentions)
Statistix 8, by SAS Institute (6 mentions)
Stata version 14, by StataCorp (6 mentions)
R version 3, by R Project for Statistical Computing (5 mentions)
Spss statistics for windows version 22, by IBM (4 mentions)
Matlab, by MathWorks (4 mentions)
Spss statistics 22, by IBM (4 mentions)
Immspss statistics version 22, by IBM (3 mentions)
Spss statistics essentials for r 22, by IBM (3 mentions)
Prism 5.0a, by GraphPad (3 mentions)
Prism v5, by GraphPad (3 mentions)
R software version 3, by R Project for Statistical Computing (3 mentions)
Stata 12, by StataCorp (2 mentions)
Sigmaplot 12, by Grafiti LLC (2 mentions)
Stata software 13, by StataCorp (2 mentions)
3 084 protocols using spss statistics version 22
1
Evaluating sSUVmax and mSUVmax Relationship
2
Particle Size and Supplementation Effects on Gene Expression
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The data were analyzed using the General Linear Models (GLM) procedure of SPSS statistics version 22 (IBM SPSS, UK) for the main effect of particle sizes, and SB supplementation along with their interactions. The main effect was analyzed with a treatment with pooled values against the other treatment. Differences between mean values were determined using LSD test at the level of P < 0.05. Correlations between FCR of the grower phase, d 10–24, and the expression levels of the genes were conducted using the procedures of SPSS statistics version 22 (IBM SPSS, UK). For correlation analysis, FCR was calculated in each pen (n = 14 birds) and the mRNA expression of the proventricular pepsinogens was measured from a randomly selected bird in each pen (n = 1 bird).
3
Multivariate Analysis of Survival Data
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All statistical analyses were performed using IBM SPSS Statistics version 22 (SPSS Inc., Chicago, IL, USA). SPSS Statistics version 22 was used for the survival analysis, univariate analysis, and multivariate logistic regression analysis. Survival analysis was performed using the Kaplan-Meier method and log-rank test. The coefficient of skewness, coefficient of kurtosis, and histogram were combined to determine whether the continuous variables fit the normal distribution. If so, we used the M ± SD to represent continuous variables. An independent sample t-test was used to compare the two groups. Means and quaternary intervals (mediums, interquartile ranges) were used to describe the continuous variables that did not conform to a normal distribution, and the nonparametric Mann-Whitney U rank-sum test was used for comparisons between the two groups. Categorical variables were displayed as the rate or constituent ratio, which were compared using the chi-square test or Fisher's exact test. Univariate analysis was used to assess the significance of each variable. All variables with statistical significance in the univariate logistic regression analysis were incorporated into a multivariate logistic regression analysis. Odds ratios (ORs) and 95% CIs were calculated. Statistical significance was set at p < 0.05.
4
Statistical Analysis of Phenotypic Traits in RILs
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Statistical analysis for the phenotypic data of PRTs was conducted using IBM SPSS Statistics Version 22 software [21 ]. The Shapiro-Wilk (w) statistic was used to test the null hypothesis that the phenotypic data were normally distributed. The univariate variance analyses were performed using standard GLM method and variance components were estimated using restricted maximum likelihood (REML) method. The broad-sense heritability for each trait across the four environment trials was calculated based on the estimated variance components with the following formula: based on plot mean and based on entry mean, where is the genotypic variance component among RILs, is the RILs × environment interaction variance component, is the residual (error) variance component, and r is the number of environment trials, n is the number of replications in each field experiment [22 ]. Correlation coefficients between each pair of the three traits were also calculated using IBM SPSS Statistics Version 22 software [21 ].
5
Predicting Mortality in Acute Pancreatitis
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The data were statistically analyzed using IBM SPSS Statistics version 22 software package. Fisher's exact test was used to compare categorical data between the two groups. Mann-Whitney U test was used to compare the two groups if continuous variables showed non-parametric features. The optimum cutoff level of the power of the FGSI and UFGSI scoring systems for predicting mortality was calculated using an ROC analysis. ROC curves were created using IBM SPSS Statistics version 22 software package. AUC, sensitivity, specificity, positive likelihood ratio (LR), negative LR, positive predictive value (PV), negative PV, and confidence intervals for these values were calculated using the Med Calc demo version. A p value of <0.05 was considered to be statistically significant.
6
Statistical Analysis of Insect Growth and Reproduction
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One-way analysis of variance (ANOVA) was performed using IBM SPSS Statistics version 22 (2013: IBM Corp., Costa Mesa, CA, USA) on percentage survival and population age distribution (at the time to first pupation and at full pupal harvest), male and female pupal weight, moth emergence and sex ratio, male and female chill coma recovery time, and female fecundity and fertility data to compare means between the different diet formulations listed in Table 1 and Table 2 . Significant means were separated with a Tukey’s HSD post hoc test, at p < 0.05. Key assumptions of ANOVA were checked and met for homogeneity of variance (using the Levene’s test, at p > 0.05) and normality (using a Shapiro-Wilk test for normality) of data distributions. A two-sample t-test (p < 0.05) was performed, using IBM SPSS Statistics version 22, on male and female mating frequency and fecundity and fertility data from the diets listed in Table 2 to compare means between chill coma exposed moths and those that were not exposed to the chilling treatment. To test whether the data sets for chill coma exposed and non-exposed moths were normally distributed, a W-test for normality was used. p values less than 0.05 were deemed to be significantly different.
7
Particle Size and Shape Analysis
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Particle size and shape distributions were tested for normality using Shapiro-Wilk and Kolmogorov-Smirnov tests. The distributions were not normally distributed. Statistical differences in the particles size distributions before and after isolation were tested using non parametric Kolmogorov-Smirnov Z test (SPSS Statistics Version 22, IBM Corp. USA). The tests were carried out on the original unbinned data for higher accuracy. Statistical differences in the aspect ratio values and roundness values before and after isolation were tested using Mann-Whitney U test (SPSS Statistics Version 22, IBM Corp. USA).
8
Comparative Analysis of Experimental Data
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Data are presented as mean + SEM. Comparison of more than two groups was performed using One-Way analysis of variance (ANOVA) or Two-Way ANOVA followed by Tukey's post-hoc test or Dunnett's post-hoc test if the data did not pass homogeneity test in IBM SPSS Statistics Version 22. Comparison of two groups was performed using Student's t-test in IBM SPSS Statistics Version 22.
9
Survival Analysis of Experimental Treatment
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Results were analyzed using one-way analysis of variance and Dunnett’s test by IBM Statistics SPSS (version 22) (IBM, NY, USA). P values were two-tailed, and the threshold for statistical significance was set at 0.05. Results are presented as mean ± standard error for all independent experiments at each time point. Kaplan-Meier method was used on survival analysis and Breslow test was used for statistical analyses by IBM Statistics SPSS (version 22) (IBM, NY, USA). Breslow P values for statistical significance was set at 0.05.
10
Statistical Analysis of Biological Data
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Data were analysed using Statistix 8 (Analytical Software, Tallahassee, Florida) and IBM SPSS statistics version 22 (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY). Data were analysed for normality using the Shapiro-Wilk test. Differences in normally-distributed data were analysed using 1-way Analysis of Variance or 2-sample t-test. Non-parametric data were analysed using a Kruskal-Wallis one-way non-parametric analysis of variance. Longevity was assessed using the Kaplan-Meier estimator, with the Log Rank test used as a measure of significance. Data were analysed at a confidence interval of 95%.
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