Values are presented as the mean ± SD. Statistical analyses were performed using IBM SPSS Statistics for Windows, version 13.0 (IBM Corp., Armonk, NY, USA). One-way analyses of variance were used to compare measured parameters among multiple groups (n>=3). A t-test was used for samples with homogeneous variance, and the Games-Howell test was used for samples with heterogeneous variance. P values are indicated by labels, and a p-value less than 0.05 was considered significant.
Spss statistics for windows version 13
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SPSS Statistics for Windows, Version 13.0 is a software application designed for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and reporting. The software is capable of handling a wide range of data types and can be used to perform various statistical procedures, including descriptive statistics, regression analysis, and hypothesis testing.
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9 protocols using spss statistics for windows version 13
1
Comparative Statistical Analysis of Experimental Parameters
2
Evaluating GeneXpert MTB/RIF Diagnostic Accuracy
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The data collection was done with Microsoft® Excel® 2007 spreadsheet, and statistical data analysis was performed with SPSS (IBM SPSS Statistics for Windows, Version 13.0) software. The study does not require a human research requiring Institutional Review Board approval.
Sensitivity, specificity and predictive values were calculated, by considering bacteriological culture as gold standard, using the 2 × 2 crosstab method on the SPSS software.
The positive and negative predictive values of GeneXpert MTB/RIF test were calculated only for the sample categories having a statistically significant size. Furthermore, comparison of the Rifampicin resistance detection performance between GeneXpert MTB/RIF and MTBDRplus was done.
Sensitivity, specificity and predictive values were calculated, by considering bacteriological culture as gold standard, using the 2 × 2 crosstab method on the SPSS software.
The positive and negative predictive values of GeneXpert MTB/RIF test were calculated only for the sample categories having a statistically significant size. Furthermore, comparison of the Rifampicin resistance detection performance between GeneXpert MTB/RIF and MTBDRplus was done.
3
Multivariate Analysis of Morphometric Traits
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Morphometric data were processed and analysed using the IBM SPSS Statistics for Windows, Version 13.0 (IBM SPSS Statistics for Windows, IBM Corporation, Armonk, NY). The descriptive statistics (mean, standard error and coe cient of variation) were calculated for all the morphometric traits under study. The Nearest Neighbor Method of Hierarchical Cluster analysis was performed to group the various body traits into clusters. A paired sample t-test was done to compare the linear traits like HW -BL; CG -PG and FL -FW.
Pearson's coe cients of correlation (r) were estimated to determine the phenotypic correlations between different body measurements, which serve as the basis for employing principal component analysis (PCA) for quantifying trait variability and characterizing breeds. PCA is a method of reducing the dimensionality of multivariate data set that accounts for the maximum portion of the variance present in the original set of variables with a minimum number of composite variables. In order to test the validity of the factor analysis of the data set, the Kaiser-Meyer-Olkin (KMO) sampling adequacy criterion and Bartlett's sphericity test were calculated at a signi cance level of 1%. The factor matrix was subjected to Varimax rotation in order to minimize the number of variables with strong correlations, especially with the rst main component.
Pearson's coe cients of correlation (r) were estimated to determine the phenotypic correlations between different body measurements, which serve as the basis for employing principal component analysis (PCA) for quantifying trait variability and characterizing breeds. PCA is a method of reducing the dimensionality of multivariate data set that accounts for the maximum portion of the variance present in the original set of variables with a minimum number of composite variables. In order to test the validity of the factor analysis of the data set, the Kaiser-Meyer-Olkin (KMO) sampling adequacy criterion and Bartlett's sphericity test were calculated at a signi cance level of 1%. The factor matrix was subjected to Varimax rotation in order to minimize the number of variables with strong correlations, especially with the rst main component.
4
Surgical Margin Accuracy in HCC Recurrence
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Receiver operating characteristic (ROC) curve was utilized to evaluate the accuracy of surgical margin to predict HCC recurrence. Area under the curve (AUC) was calculated as measurements of the accuracy of the test. The Kaplan–Meier survival analysis (log-rank test) was used to analyze RFS time. The factors with P < 0.05 from the Kaplan–Meier analysis were enrolled in the Cox regression hazard model. Categorical variables were compared using the χ2 test or Fisher exact test. P values were 2 tailed. Statistical significance was accepted for P values <0.05. All of the data were statistically analyzed with the software SPSS Statistics for Windows, version 13.0 (SPSS Inc, Chicago, IL).
5
Triplicate Experiments with ANOVA Analysis
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All experiments were performed in triplicate, and the results are reported as the mean ± standard deviation (SD). Statistical differences between the control and experimental groups were determined by one-way ANOVA using the SPSS Statistics for Windows version 13.0 (SPSS, Chicago, IL, USA); the results were considered statistically significant at a p value less than 0.05.
6
Predicting Sarcopenia Using Serum GDF15
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Statistical analyses were performed using SPSS software (SPSS Statistics for Windows, Version 13.0. Chicago: SPSS Inc.). Continuous variables are expressed as the mean values and standard deviation. Pearson’s correlation coefficient was calculated to examine the association between continuous variables. The strengths of the correlations were weak (0–0.25), moderate (> 0.25–0.50), strong (> 0.50–0.75), and very strong (> 0.75) (35 (link)). Differences between two groups were assessed using Student’s t-test (normally distributed data) or the Mann–Whitney test (non-normal distribution). p-values < 0.05 were considered statistically significant. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to determine the ability of serum GDF15 to predict sarcopenia. R software (version 3.4.0) and the R package “rms” were used to establish the nomogram model. ROC analysis and calibration plots were used to assess the accuracy of the predictive ability of the nomogram.
7
Identifying Factors Affecting In-Hospital Mortality
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Categorical variables are presented as numbers and percentages and were compared using the chi-squared test or Fisher’s exact test, as appropriate. Continuous variables are presented as means and standard deviations or as medians and ranges. The student’s t-test and the Mann–Whitney U test were conducted on normally and nonnormally distributed continuous variables, respectively. Univariable and multivariable logistic regression models were used to identify the variables associated with in-hospital mortality. The multivariable logistic regression model was assessed by the likelihood ratio test (Cox Snell R2 = 0.3030, p < 0.001). The odds ratio (OR) is reported with a 95% confidence interval (CI). The area under the receiver operating characteristic curve (AUROC) was determined to evaluate the ability of the TTAS to predict in-hospital mortality and medical resource utilisation among younger and older patients. The available case analysis method was used to handle data not missing at random. The total number of observation (n) of each variable was presented. A p-value < 0.05 was considered statistically significant. Analyses were conducted using SPSS Statistics for Windows, version 13.0 (SPSS Inc., Chicago, IL, USA).
8
Ovariectomy and Chronic Stress Effects
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The distribution of data was determined by Shapiro-Wilk test. As the distribution was not normal, comparisons of specific sets of two subgroups were conducted using Mann-Whitney test. The data used for comparison are shown in Supplemental Tables. The following sub-groups were compared: (A) NON-OVX-C and OVX-C groups were compared to observe the effect of ovariectomy, (B) NON-OVX-C and NON-OVX-S groups were compared in order to notice changes due to chronic stress exposure, (C) OVX-C and OVX-S groups were compared to determine the effect of chronic stress in ovariectomized female rats, particularly to determine if the direction of changes was similar as in non-ovariectomized females, (D) NON-OVX-S and OVX-S groups were compared to observe the influence of ovariectomy on the response to chronic stress and (E) NON-OVX-C group was compared to OVX-S group in order to reveal combined impact of ovariectomy and chronic stress. Statistical tests were performed using the statistical software package SPSS (SPSS Inc. Released 2008. SPSS Statistics for Windows, Version 13.0, Chicago, IL, USA). Statistical significance level was set at P < 0.01.
9
Acoustic Absorbance Assessment Using Handheld Tympanometer
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The device used in the study was a Titan handheld tympanometer (Interacoustics, Middelfart, Denmark), which was purchased using the FAPESP grant n° 2014/15810–0. The protocol defined for this study adopted the following set-up configurations:
The WBT test provided the individual resonance frequency, conventional tympanometry results at 226 Hz, and also acoustic absorbance at frequencies in the 226–8,000 Hz range in two modalities: under ambient pressure and pressurized conditions.
The acoustic absorbance data of each participant were obtained using WBT research files on Microsoft Excel (Microsoft Corp., Redmond, WA, USA). The absorbance frequencies selected for analysis were based on data reported by Aithal et al.
22 (link)
The following frequencies were selected: 226 Hz, 257 Hz, 324 Hz, 408 Hz, 500 Hz, 630 Hz, 794 Hz, 1,000 Hz, 1,260 Hz,1,587 Hz, 2,000 Hz, 2,520 Hz, 3,175 Hz, 4,000 Hz, 5,040 Hz, 6,350 Hz, and 8,000 Hz, amounting to a total of 17 frequencies analyzed per ear.
The data were analyzed by the statistics section of the institution, using descriptive analysis statistics techniques and the Mann-Whitney, Kruskal-Wallis, and Wilcoxon tests, with the SPSS Statistics for Windows, Version 13.0 (SPSS Inc. Chicago, IL, USA). The significance level of 5% was adopted for rejection of the null hypothesis.
The WBT test provided the individual resonance frequency, conventional tympanometry results at 226 Hz, and also acoustic absorbance at frequencies in the 226–8,000 Hz range in two modalities: under ambient pressure and pressurized conditions.
The acoustic absorbance data of each participant were obtained using WBT research files on Microsoft Excel (Microsoft Corp., Redmond, WA, USA). The absorbance frequencies selected for analysis were based on data reported by Aithal et al.
22 (link)
The following frequencies were selected: 226 Hz, 257 Hz, 324 Hz, 408 Hz, 500 Hz, 630 Hz, 794 Hz, 1,000 Hz, 1,260 Hz,1,587 Hz, 2,000 Hz, 2,520 Hz, 3,175 Hz, 4,000 Hz, 5,040 Hz, 6,350 Hz, and 8,000 Hz, amounting to a total of 17 frequencies analyzed per ear.
The data were analyzed by the statistics section of the institution, using descriptive analysis statistics techniques and the Mann-Whitney, Kruskal-Wallis, and Wilcoxon tests, with the SPSS Statistics for Windows, Version 13.0 (SPSS Inc. Chicago, IL, USA). The significance level of 5% was adopted for rejection of the null hypothesis.
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