Results are expressed as the mean ± standard error of the mean (SEM). Differences were considered to be significant at P<0.05. Analysis of the data was performed using Student’s t-test on IBM® SPSS® Statistics, v 18.0 (Quarry Bay, Hong Kong).
Spss statistics v18
Manufactured by IBM
Sourced in United States, United Kingdom
SPSS Statistics v18 is a comprehensive statistical software package used for data analysis, data management, and data documentation. It provides a wide range of statistical techniques for exploring, visualizing, and analyzing data. The core function of SPSS Statistics v18 is to enable users to perform advanced statistical analyses, generate reports, and make data-driven decisions.
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Lab products found in correlation
27 protocols using spss statistics v18
1
Comparative Analysis of Molecular Responses
2
Statistical Analysis of Experimental Groups
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Statistical analysis was performed using SPSS Statistics v18.0 (IBM, NY, USA). Differences among the three groups were analyzed using one-way ANOVA and multiple comparisons were performed using Tukey's method. Differences between the two groups were analyzed using a two-tailed paired Student's t-test. Non-normally distributed data use the rank sum test, Kruskal-Wallis H test, or Mann-Whitney U test. All data referenced above were presented as the means ± SEM. Statistical significance was defined as *p < 0.05.
3
Quantitative PCR Normalization and Analysis
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A Bio-Rad instrument (CFX Connect, USA) and the accompanying software (Bio-Rad CFX Manager) were used for qPCR data normalization, and the relative quantities of endosymbionts were calculated using the 2−ΔΔct method. The differences were evaluated in IBM SPSS Statistics v.18.0. For all ANOVA analysis, independent-samples t-test, data were checked for homogeneity by the Levene’s test. Multiple comparisons of means were assessed by Tukey’s HSD test at a significance level α = 0.05. Figures were generated using Sigma Plot 14.0. Error bars in all graphs represent standard error.
4
Statistical Analysis of Research Data
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Data management and analysis were performed using the IBM SPSS Statistics V18.0 software package (IBM Corp., Armonk, NY, USA), and the results were presented as the mean and pooled standard error. Before performing the difference analysis between groups, the Shapiro–Wilk test was performed to verify the normality of the data. When the variable data presented a non-normal distribution, a one-way ANOVA, followed by the Kruskal–Wallis test, was used for analysis with multiple FDR corrections. When the data presented a normal distribution, a one-way ANOVA analysis followed by an LSD test was used. Differences were considered significantly expressed at a P-value of < 0.05 and with a significant tendency at 0.05 ≤ P < 0.10.
5
Analyzing Virologic and Immunologic Data
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Student's t-test and analysis of variance (ANOVA) were used to analyse all of the virologic and immunologic data for significant differences (p < 0.05). The statistical analyses were performed in IBM SPSS Statistics v18.0 (Chicago, IL, USA).
6
Comparative Viral Genome Analysis
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The Student’s t-test and ANOVA test were used to determine if there were significant differences (p < 0.05) for all tested viruses. The statistical analyses were performed in IBM SPSS Statistics v18.0 (Chicago, IL, USA).
7
Comparative Analysis of Neurological Biomarkers
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All data were presented as the mean ± standard deviation (SD). ANOVA with Tukey's post hoc test was used to determine the statistical significance. SPSS Statistics v18 (IBM Corp., Armonk, NY, USA) was used as the statistical analysis software. A value of P<0.05 was considered to indicate a statistically significant difference.
8
Quantitative Analysis of Gene Expression
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All experiments were conducted at least three times. The data represent the mean ± standard deviation of three independent experiments. Statistically significant differences between control and treatment groups were determined by Student’s t-test. SPSS Statistics v18 (IBM Crop., Armonk, NY, USA) was used as the statistical analysis software. A p-value of less than 0.05 was considered statistically significant.
9
Survival Analysis of Mortality Predictors
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Continuous variables were described using means±SD. Categorical variables were described by frequencies and percentages. Differences between independent groups were tested using the χ2 test for categorical variables or the t test. In cases where the samples were paired, the paired t test was used. Survival rates at 2 years were graphed using the Kaplan-Meier method. A Cox regression model was used to estimate HRs and 95% CIs of baseline characteristics on mortality. Owing to the relatively low number of patients, no multivariable analyses were performed in the present study. Analyses were conducted with IBM SPSS Statistics V.18 (IBM Corporation, Somers, New York, USA) and tested using two-sided tests at a significance level of 0.05.
10
Triplicate Statistical Analysis Protocol
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All experiments were performed in triplicate and the data are presented as means ± standard deviation (n = 3). Statistical analysis was carried out using SPSS software (SPSS Statistics v18, IBM, Chicago, IL, USA). Statistically significant differences between variables were assessed via one-way analysis of variance (ANOVA) followed by Student’s t-test (* p < 0.05).
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