All of the statistical tests were performed using SPSS 9.0 (IBM, Chicago, IL), or Sigma plot. The significance of the differences among multiple groups were analyzed via one‐way ANOVA followed by Fisher's least significant difference (LSD). Comparisons for two groups of data were done by Student's t test. Values were expressed as mean ± SEM. Results were considered statistically significant at a p‐value of less than 0.05.
Spss 9
Manufactured by IBM
Sourced in United States
SPSS 9.0 is a software application for statistical analysis. It provides tools for data management, analysis, and presentation. The core function of SPSS 9.0 is to enable users to analyze and interpret data.
Automatically generated - may contain errors
23 protocols using spss 9
1
Statistical Analysis of Experimental Data
2
Statistical Analysis of Experimental Data
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Data were analyzed using SPSS 9.0 statistical package (IBM, Armonk, NY, USA).
Results are expressed as mean ± SD. Differences between means were tested using
Student’s t-test. Differences were considered significant at
P < 0.05.
Results are expressed as mean ± SD. Differences between means were tested using
Student’s t-test. Differences were considered significant at
P < 0.05.
3
Surgical Time Estimation for Lower Jaw Procedures
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The data on lower jaw surgery were received on the cards designed to collect the different extraction characteristics. All the data were coded in a table with SPSS 9.0 (IBM, Armonk, NY, USA), on which statistical methods were applied using the software.
A complete descriptive study was carried out detailing all the variables.
Multiple linear regression using the backward method was used to estimate the time in minutes required for the intervention from the values of various continuous or discrete variables.
It is indicated with the usual format (p < 0.05; p < 0.01; p < 0.001, p < 0.0001, and p < 0.00001); the lower the figure, the higher the significance.
A complete descriptive study was carried out detailing all the variables.
Multiple linear regression using the backward method was used to estimate the time in minutes required for the intervention from the values of various continuous or discrete variables.
It is indicated with the usual format (p < 0.05; p < 0.01; p < 0.001, p < 0.0001, and p < 0.00001); the lower the figure, the higher the significance.
4
Statistical Significance Analysis Protocol
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Statistical significance analyses were analyzed by using the statistical software (software version: SPSS 9.0, the IBM corporation, Armonk, NY, USA). The statistical significance was analyzed by Bonferroni correction with or without two-way analysis of variance (ANOVA).
5
Statistical Analysis of Biological Replicates
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In the presence work, the biological and technical replicates have been carried out for all experiments and the results were from triple repeats with similar results. All statistical significance analyses were performed using SPSS 9.0 statistical software (IBM Corporation, Armonk, NY, USA). The IC50 values were calculated using Origin software (Origin 6.1; OriginLab Corporation, Northampton, MA, USA). Statistical significance was analyzed using Bonferroni correction with two-way ANOVA, and paired samples were tested using the paired-sample t-test (SPSS Statistical Software v16.0; SPSS Inc., Chicago, IL, USA).
6
Statistically Analyzing Antitumor Agents
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Statistical analysis in the present work was performed by using Bonferroni’s correction without two-way ANOVA (SPSS 9.0, IBM, Armonk, NY, USA). The IC50 values of antitumor agents were calculated by the Origin software (version No.: 6.1; OriginLab, Northampton, MA, USA). A p < 0.05 was considered statistically significant between groups.
7
Statistical Analyses of LUSC Cell Assays
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The statistical analyses were performed using the SPSS 9.0 software (IBM, Armonk, NY, United States). Differences among groups were assessed (Bonferroni correction with two-way ANOVA or paired-sample t-test). The IC50 values of agents on LUSC cells were calculated using the Origin 6.1 software (OriginLab, Northampton, MA, United States).
8
Statistical Analysis of Flow Cytometry Data
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IBM SPSS 9.0 (obtained at https://www.ibm.com/cn-zh/spss , accessed on 1 October 2023) was employed for statistical analysis. Firstly, we tested whether the data followed a normal distribution and variance homogeneity. Then, we selected the appropriate statistical method. Flow cytometry data analysis was carried out using FlowJoTM10 (https://www.flowjo.com/solutions/flowjo/downloads , accessed on 1 October 2023).
9
Glaucoma Diagnosis and Types Prediction
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Based on the number of patients in the study, the statistical power this study provided was 0.893 for the result of presence or absence of glaucoma diagnosis and 0.983 for the result of the specific types of glaucoma. Models were generated in SPSS 9.0 (IBM Corporation, Armonk, NY, USA) using binomial and multinomial logistic regressions with generalized estimating equations (GEE). GEE was used because both eyes were used for most of the patients, requiring an adjustment for multiple data points from the same subject. Multiple binomial and multinomial regressions were done in a backward stepwise manner using linear and logistic regression respectively. Results included P-values and odds ratios (ORs) with confidence intervals (CIs) as presented in the Results section.
10
Comparison of HHP Treatment Impacts
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All analyses were repeated twice, except for particle size measurement, which was repeated 3 times. Data were subjected to ANOVA to determine whether there were any statistically significant differences (P ≤ 0.05) between the untreated control and different HHP treatment regimens (SPSS 9.0, IBM Spain). Tukey's test was used for multiple post hoc comparisons.
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