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Spss software v 18

Manufactured by IBM
Sourced in United States

SPSS software v.18.0 is a statistical analysis tool developed by IBM. It is designed to assist users in data management, analysis, and visualization. The software provides a range of statistical techniques and methods to help users understand and interpret data.

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52 protocols using spss software v 18

1

Statistical Data Analysis on SPSS

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Data were analyzed using SPSS software v18.0 (SPSS Inc., Chicago, IL, USA). Data were presented as means ± SD or n (%), and compared using independent sample tests or the χ2 test. All statistical tests were two-sided, and a P value of <.05 was considered to indicate statistical significance.
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2

Soil Microbial Responses to Pavement Depth

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One-way ANOVAs and two-way ANOVAs were used to analyze depth and pavements effects on soil physio-chemical and microbial properties (LSD; P = 0.05). Linear models were conducted to verify relationships between bacterial diversity and soil properties. We determined bacterial community similarity among pavements and soil depth using ANOSIM; non-metric multi-dimensional scaling (NMDS) was used to visualize clusters. Canonical correspondence analysis (CCA) was performed to show a visual relationship between environmental factors and bacterial distributions. The resulting clustering trees were paired with a heatmap of abundance data, created with ‘heatmap.2’ from the ‘gplots’ package. The above statistical analyses were performed using the vegan package of R v.3.1.1 [28 ] and SPSS software v.18.0 (SPSS Inc., Chicago, IL, USA).
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3

Collaterals and Infarct Volume Assessment

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The number of collaterals, diameter of collateral arteries, infarct volume, and NSS were compared between groups by Student's unpaired t-test using SPSS software (v18.0, SPSS Inc., Chicago, IL); the values are presented as the mean±SD. For the multiple time point test, one-tailed ANOVA followed by Bonferroni's t-test was used. A probability value of less than 0.05 was considered to represent statistical significance.
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4

Outcomes of Recurrent Breast Cancer

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Follow-up data available as of Dec 31, 2017, were analyzed. The locoregional control (LRC) was defined as freedom from clinical or radiographic evidence of locoregional failure (LRF) within the ipsilateral CW and/or regional draining lymphatics after treatment with radiation for initial ILRR. The distant metastasis-free survival (DMFS) was measured from the date of diagnosis of ILRR to the date of DM. The disease-free survival (DFS) was measured from the date of diagnosis of ILRR to the date of second ipsilateral LRR, DM, or death attributable to breast cancer and/or second primary nonbreast cancer. The OS was defined as the time from the diagnosis of ILRR to death attributable to breast cancer, cause other than breast cancer, or unknown cause at the last follow-up date. Statistics were performed using SPSS software (v18.0). The probabilities of LRC, DMFS, DFS, and OS were calculated using the Kaplan-Meier product-limit method, and compared between groups using the log-rank test. The influence of primary tumor characteristics, recurrence patterns, and treatment-related factors after ILRR on LRC, DFS, and OS were tested by univariate and multivariate analysis using forward step-wise Cox regression method. All p values were two-sided and if less than 0.05 were deemed significant.
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5

Identifying Predictors of Healthcare Expenditure

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Continuous variables are reported as means±SDs. For the univariate analysis, we used χ2 tests to compare categorical variables and analysis of variance to compare continuous variables. To identify independent predictors of increased healthcare expenditure, we used Multivariate logistic regression. Variables were entered in the model one by one and retained when their significance was <0.10. To evaluate the discriminatory ability of the resulting predictive model for identifying patients with cirrhosis with healthcare expenditures ≥85th centile, we calculated the area under the receiver operating characteristic (ROC) curve.15 (link) Goodness of fit of the model was evaluated using the Hosmer-Lemeshow method. Statistical analyses were performed using SPSS software, V.18.0. All statistical tests and CIs were constructed with a type I error level of 5% and P values <0.05 were considered statistically significant.
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6

Analyzing TBBPA Effects on Metamorphosis

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RT-qPCR data were present as means ± standard error of the mean (SEM), other quantitative data were shown as means ± standard deviation (SD). SPSS software v 18.0 (USA) was used for statistical analysis with the tank as the statistical unit. Two-way analysis of variance (ANOVA) followed by Dunnett’s test was employed for all data analysis, except the Chi-square test for the development stage distribution of spontaneous metamorphosis assay among TBBPA treatments.
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7

Cliff Zone Preference Assay in Mice

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The test consists of a transparent plastic box placed in a table with the four edges emerging about 20 cm above the top of the table and a rod running along the middle, separating the box into two sections: one has a checker paper placed on the top surface of the box, whereas the other has the same checker paper placed on the bottom surface of the box. The mouse was placed on the rod and should choose within 5 min to walk on the safe side, where the checker paper is on the top surface of the box or the cliff zone (“fake cliff”) where the checker paper is on the bottom surface of the box. Each mouse is tested for 10 trials with the apparatus turned 180° after five trials. Results are expressed in percentages of chosen zones for each mouse.
Statistics were done using SPSS Software, v18.0. One or Two-Way analysis of variance (ANOVA) followed by Bonferroni post-hoc comparisons was used to compare the mean differences with multiple factors, i.e., between groups (genotypes), and/or intervals (days), as appropriate. Unpaired Student t-test was used when only two groups were compared, unless specified otherwise. Equality of variances was assessed by Levene's Test. Statistical significance was considered only when p < 0.05.
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8

Gene Expression and Clinicopathological Correlation

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Numerical data were expressed as mean ± SD GraphPad Prism v.5.0 (GraphPad Software, Inc., La Jolla, CA, USA) and SPSS software v.18.0 (SPSS, Inc., Chicago, IL, USA) was used for statistical analysis. A Student's t-test was performed to compare two groups of gene expression. The correlation between mRNA expression and clinicopathological features was assessed using the χ2 test and Student's t-test. Survival analysis was performed through the Kaplan-Meier method and the log-rank test was used to evaluate the statistical significance of the differences. P<0.05 was considered to indicate a statistically significant difference.
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9

Population-based COPD Outcomes Analysis

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The study outcomes are described for the entire population of patients with COPD. Comparisons among the four subgroups defined by GMA health risk grades were done. The results are summarised in the main manuscript and complementary information is reported in detail in the online supplementary material. In this population-based analysis (table 1), age and number of chronic comorbidities are summarised as mean and SD, the proportion of women and the morbidities are expressed as percentages, while mortality rate, hospitalisation rate and COPD-related hospitalisation rate are expressed per 100 patients with COPD among patient groups. Comparisons among groups were done using analysis of variance for continuous variables, and χ2 test for binary and nominal variables.
Statistical analyses were performed using SPSS software V.18.0. All statistical tests and confidence intervals were constructed with a type I error (alpha) level of 5%, and P values lower than 0.05 were considered statistically significant.
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10

Statistical Analysis of Biological Data

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Data are presented as mean ± standard error of the means (SEM). One-way analysis of variance (ANOVA) followed by Tukey post-hoc test using the Statistical Package for the Social Sciences (SPSS software V. 18.0) were performed for statistical analysis. P values < 0.05 were considered as the significant level.
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