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Spss statistics software v 22

Manufactured by IBM
Sourced in United States

SPSS Statistics software v. 22 is a data analysis tool developed by IBM. It provides a comprehensive set of features for statistical analysis, data management, and visualization. The software is designed to help users efficiently analyze and interpret complex data from a variety of sources.

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39 protocols using spss statistics software v 22

1

Automated Statistical Analysis of IHC Data

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All online statistical analyses were performed automatically by the cBioPortal platform, and P values < 0.05 and q values < 0.05 were accepted as statistically significant. Immunohistochemical results were analyzed using IBM SPSS statistics software V 22.0. Pearson χ2 test or Fisher’s exact test was used for comparison of categorical variables, and P < 0.05 was statistically significant.
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2

Behavioral Experiments in Sleep States

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All data on behavioral experiments in the “Matching” and “Mismatching” conditions were restricted to trials with response times (RT) >300 ms and <2000 ms. Behavioral data recorded in the 8 h TIB, TSD, and TIBR states were statistically tested via repeated measures analysis of variance (ANOVA), with Greenhouse-Geisser correction for non-sphericity and Bonferroni post hoc analysis. Statistical tests were carried out using IBM SPSS Statistics software (V 22.0). Data are represented as mean and standard deviation (SD).
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3

Comparing Running Biomechanics

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The Mann-Whitney U test was used to compare body characteristics and analyze variables between good and poor groups. In addition, the Wilcoxon signed-rank test was used to separately identify significant differences between straight and curved path running. For multiple comparisons, we used a Bonferroni correction to set the significance level at p < 0.05, and Cohen’s d was used to describe effect size (Cohen, 1992 (link)). All statistical analyses were performed using IBM SPSS Statistics software (v. 22.0, SPSS Inc., Chicago, Illinois, USA).
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4

Statistical Analysis of Osteoclastogenesis

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Statistical analyses were performed using SPSS Statistics software V. 22.0 (IBM, Armonk, NY, USA). We obtained means and standard deviations. In the osteoclastogenesis study, group means were compared using the Kruskal–Wallis test. The Wilcoxon’s signed-rank test was used to determine significant differences between fibrinogen and citrullinated fibrinogen in osteoclast gene expression.
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5

Primary Culture Neurological Evaluation

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Data were generated from at least three independent primary culture experiments. Data are presented as the mean ± standard error of the mean (SEM) and were analyzed by one-way analysis of variance followed by the least significant difference test using SPSS Statistics software v22.0 (IBM, Armonk, NY, USA) unless stated otherwise. The evaluation of neurological function was analyzed by two-way analysis of variance followed by the least significant difference test. P < 0.05 was considered statistically significant.
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6

Non-Parametric Statistical Analyses

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Non‐parametrical statistical analyses with exact significance values were used for all group comparisons. Comparisons between the genotype groups were performed using the independent‐sample Mann–Whitney U test. Differences between the various age groups were evaluated with the independent‐sample Kruskal–Wallis test. Post hoc analysis between specific age groups was performed using the independent‐sample Mann–Whitney U test with a Bonferroni correction for multiple comparisons. Outliers with a high coefficient of variation (≥20%) between duplicate measurements were excluded from statistical analysis. All statistical tests were performed using SPSS statistics software v22.0 (IBM). All graphs were created using Graphpad Prism v5.03 (Graphpad, San Diego, CA, USA).
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7

Comparative Statistical Analysis Protocol

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Statistical analysis was performed using Mann–Whitney U tests, using SPSS Statistics software V. 22.0 (IBM, Armonk, NY, USA). Data are presented as the mean ± standard error of the mean (SEM). p-values less than 0.05 were considered statistically significant.
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8

Survival Analysis of GBM Proteins

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Survival analysis was performed using the Kaplan-Meier estimator and log-rank test to assess the significant association of immunopositive versus immunonegative ATRX, p53, and IDH1 proteins with overall survival (OS) and progression free survival (PFS) times in GBM patients. A P < 0.05 was considered statistically significant. Analyses were carried out using IBM SPSS Statistics software V22.0.
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9

Nonparametric Analysis of Genotype-Age Interactions

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Non-parametrical statistical analyses with exact significance values were used for all group comparisons. Comparisons between the genotype groups were performed using the independent-samples Mann-Whitney U test. Differences between the various age groups were evaluated with the independent-samples Kruskal–Wallis test. Post hoc analysis between specific age groups was performed using the independent-samples Mann–Whitney U test with a Bonferroni correction for multiple comparisons. All statistical tests were performed using SPSS statistics software v22.0 (IBM, Amrock, NY, USA). All graphs were created using Graphpad Prism v5.03 (Graphpad, San Diego, CA, USA).
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10

Prognostic Value of p53 Expression

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The defined event was death from any cause. PFS was defined as the time between date of first treatment and date of tumor progression and OS was defined as time from diagnosis of locally advanced or metastatic disease until date of death; or if event was not found, censored at date of last observation.
Kaplan-Meier plots were used for PFS and OS analysis, and the log-rank test was used to compare curves separated according to expression of p53. Cox proportional regression was performed for the estimation of hazard ratios (HRs) and confidence intervals (CIs).
Spearman correlation was used to assess the correlation of p53 protein expression to clinicopathological variables. For the statistical analyses all variables were dichotomized: p53 IR vs. non-IR, age ≤60 years vs. >60 years, Ki67 ≥55% vs. ≤55% LDH normal vs. high and performance status ECOG 0+1 vs. 2+3.
All statistical analyses were performed using IBM SPSS statistics software (v22, USA).
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