Statistical analysis was performed with SPSS 19.0 statistics software. Both Student’s t test and ANOVA two ways test were used to evaluate the differences between groups, and P<0.05 or less was considered significant.
Spss 19.0 statistic
Manufactured by IBM
Sourced in United States
SPSS 19.0 is a software application developed by IBM for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and reporting. The core function of SPSS 19.0 is to enable users to perform a wide range of statistical tests and procedures, including descriptive statistics, regression analysis, factor analysis, and more. The software is designed to be user-friendly and offers a graphical user interface for easy navigation and data visualization.
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Lab products found in correlation
33 protocols using spss 19.0 statistic
1
Statistical Analysis of Experimental Data
2
Statistical Analysis of Categorical and Continuous Variables
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Categorical variables were tested by a χ2 statistic unless expected cell counts were smaller than five, in which case Fisher's exact test was used. Students’ T-test was used for the analysis of continuous variables with a normal distribution (age, body mass index). The SPSS 19.0 statistics software package was used for statistical analysis. Comparisons were adjusted for age and sex in a logistic regression model.
3
Comprehensive Statistical Analysis Protocol
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All data were analyzed with SPSS 19.0 statistics software. Enumeration data were analyzed with chi-square test. Measurement data were expressed by mean ± SD. Comparison between groups was performed with one-way analysis of variance. Variance was converted before statistical analysis if it was not homogeneous. Difference was considered to have statistical significance if P<0.05.
4
Statistical Analysis of Student Responses
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Students’ responses were unloaded, and appropriate statistical treatments were performed using the SPSS 19.0 statistics software to test the frequency distribution and mean reports. ANOVA variance analysis, t-test, and Pearson correlation coefficient were applied to test for statistical significances between various measures in this study, and mean values were used to perform principal component analysis (PCA) to test whether the variables are correlated in the population. The significance level was accepted at P < 0.05.
5
Statistical Analysis of Categorical and Continuous Data
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Fisher’s exact test was performed for the analysis of categorical variables and Students’ t-test for the analysis of continuous variables. The SPSS 19.0 statistics software package was used for statistical analysis.
6
Statistical Analysis of Experimental Data
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SPSS19.0 statistics software was used to process data. Measurement data were expressed as Mean±SD. Data was compared using t test and one-way analysis of variance. Difference was considered to be statistically significant if P<0.05.
7
Comparative Statistical Analysis of Treatment
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Statistical analyses were performed using SPSS 19.0 statistics software (SPSS Inc., Chicago, IL). Independent two-sample t-test was used to compare the change of the variables between two groups. Paired t-tests in both groups were employed to analyze the results of improvement differences from baseline (preoperatively) to final followup. Categorical variables were compared by Chi-square, Fisher's exact test or Mann–Whitney U test. All statistical assessments were two sided and evaluated at the 0.05 level of significant difference. Descriptive statistics in the form of mean and standard deviation.
8
Cell Proliferation Analysis in Microgrooved Surfaces
9
Analyzing NSCs across Altitudes
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The mean value of NSCs released in the same treatment group were calculated using the SPSS 19.0 Statistics (SPSS Inc., Chicago, MI, USA) software. Differences of NSC contents in the same treatment group between different altitudes were determined using one-way ANOVA and post hoc least significant difference (LSD) tests. Topsoil and canopy litter responses were analyzed using a two-factor ANOVA, with altitude and decomposition time as fixed factors. Origin (version 2021) was used to draw the point line graphs and a heatmap of the correlation between the NSCs and the C, N, and P contents.
10
Sagittal Radiographic Analysis of Juveniles
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Statistical analysis was performed using SPSS 19.0 statistics software (SPSS Inc, Chicago, IL). Descriptive statistics were listed in the form of mean ± standard deviation (SD). Radiographic sagittal parameters for juvenile and adolescent group were compared using independent samples t test.
Weight mean difference (WMD) with 95%CI was used to explore the pooled results of sagittal parameters. Sub-group analyses were also performed according to ethnicity rather than the institutions where these study populations received their examinations. Furthermore, volunteers were also divided into two groups according to the age, and meta-analyses were performed in juveniles and adolescents, respectively. The heterogeneity of included studies was examined by a chi-squared-based Q statistical test and quantified by I2 metric value. If I2 value was more than 50% or P < 0.10, WMD were pooled by the random effect model; otherwise, the fixed effect model was used. Sensitivity analysis was performed to assess the impact of each study on the combined effect of the present meta-analysis. Publication bias was also performed to detect publication bias existed in this study.
Revman 5.3 software was employed and a P < 0.05 was considered as statistically significant.
Weight mean difference (WMD) with 95%CI was used to explore the pooled results of sagittal parameters. Sub-group analyses were also performed according to ethnicity rather than the institutions where these study populations received their examinations. Furthermore, volunteers were also divided into two groups according to the age, and meta-analyses were performed in juveniles and adolescents, respectively. The heterogeneity of included studies was examined by a chi-squared-based Q statistical test and quantified by I2 metric value. If I2 value was more than 50% or P < 0.10, WMD were pooled by the random effect model; otherwise, the fixed effect model was used. Sensitivity analysis was performed to assess the impact of each study on the combined effect of the present meta-analysis. Publication bias was also performed to detect publication bias existed in this study.
Revman 5.3 software was employed and a P < 0.05 was considered as statistically significant.
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