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Spss 22.0 statistics software

Manufactured by IBM
Sourced in United States

SPSS 22.0 is a comprehensive statistical analysis software package developed by IBM. It provides a wide range of statistical techniques and tools for data analysis, including descriptive statistics, bivariate analysis, multivariate analysis, and modeling. SPSS 22.0 is designed to help users analyze complex data, identify patterns, and make informed decisions.

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25 protocols using spss 22.0 statistics software

1

Statistical Analysis of Clinical Data

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The SPSS 22.0 statistics software (IBM SPSS Statistics, IBM Corp, Somers, New York) was used to analyze demographic and clinical features. Continuous data were expressed as mean and standard deviation (mean ± SD), and the Shapiro–Wilk test was used to verify whether the data were normally distributed. Between-group comparisons were performed using the two independent sample t-tests or Mann–Whitney U tests for normally distributed data and non-normal data, and within-group comparisons were performed using the linear mixed model. The chi-squared test (χ2) test was used for categorical variables. The significance level was set at P < 0.01 (two-tailed).
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2

Statistical Methods for Biochemical Analyses

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Continuous variables with normal distributions were presented as mean ± standard deviations (SDs) and continuous variables with nonnormal distributions were shown as medians and interquartile ranges. The differences between groups were assessed by an independent t-test (for age and weight) and the Mann–Whitney U-test (for disease duration and other biochemical indexes). Counts and percentages were calculated for categorical variables and compared by Fisher's exact test. All statistical assessments were two-sided and evaluated at the 0.05 level of significance. All analyses were performed using SPSS 22.0 statistics software (IBM SPSS Inc., Chicago, Illinois, USA).
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3

Statistical Analysis of Experimental Data

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SPSS 22.0 statistics software (IBM SPSS Inc., Chicago, IL, USA) was uses for statistical analyses. The data are expressed as mean ± standard error (SE). Comparison within groups was made by ANOVA for repeated measures (or independent t-test when only 2 groups were compared). The least-significant difference (LSD) was used for post hoc test. A value of P < 0.05 was considered significant.
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4

Statistical Analysis of Experimental Data

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All data were analyzed with SPSS 22.0 statistics software (Chicago, USA). The data are shown as the means ± standard error of the mean (SEM). Significant differences between more than 2 groups were determined using one-way analysis of variance (ANOVA). Comparisons between 2 groups were made using the 2-tailed unpaired t test (* P<0.05; ** P<0.01).
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5

Statistical Analysis of Grouped Experiments

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SPSS22.0 statistics software was used for all statistical analyses. Each grouped experiment was repeated 3 times and the averaged data from each group were compared with the other groups. The data are presented as the mean ± SD. Statistical analyses were carried out using ANOVA followed by the Dunnett’s post hoc test. Differences were considered statistically significant at p < 0.05.
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6

EGFR ex20ins Mutational Landscape

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The EGFR ex20ins data from the other cohorts were collected from other research (7 (link),9 (link)-12 (link),19 (link)-21 (link)). SPSS 22.0 statistics software (SPSS, Inc., Chicago, IL, USA) was used for the statistical analysis. The Chi-square (χ2) test was used to compare the rates among groups with different features. Hierarchical clustering analyses were performed based on Euclidean distance and the average-linkage method. The statistical tests were 2-sided, and the significance level was set as P<0.05.
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7

Radiological Evaluation of Degenerative Disc Disease

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We performed statistical analyses using Statistic Package for Social Science (SPSS) 22.0 statistics software (SPSS Inc., Chicago, IL), and listed measurement data in the form of mean and standard deviation (SD), enumeration data in terms of ratio. Chi-square test was used for enumeration data comparison between the DDD group and the normal group. Independent two-sample t-test and Wilcoxon rank sum test were used to compare the differences of measurement data between the two groups. The general characteristics which were different between the two groups(P < 0.05) were used as a covariate to perform comparisons between radiological parameters and the level of significance was set at p < 0.05. For each parameter, diagnostic screening and confirmatory tests were performed by calculating true positive, true negative, false positive, false negative values, sensitivity and specificity of different cut-off points were calculated. ROC analysis was performed and AUC were calculated for radiological parameters to see which one had high discrimination performance with AUC being greater than 0.9 for the DDD with compensatory sagittal balance. The maximal Youden index (maxYI) of each parameter was also calculated. The cut-off point corresponding to it was taken as the threshold value that determine the optimal sensitivity-specificity pair.
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8

Antifungal Activity of Terbinafine Against Botrytis cinerea

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The effect of terbinafine hydrochloride on mycelial growth against B. cinerea was determined by the mycelial growth rate method [31 (link)]. The terbinafine was dissolved by 0.5% dimethyl sulfoxide (DMSO). The mycelial plugs were cut from the edge of the 5-day-old colony and then placed in sterile potato dextrose agar (PDA) with terbinafine at final concentrations of 0.20, 0.39, 0.78, 1.56, 3.13, and 6.25 mg·L−1. PDA medium containing an equal concentration of boscalid, which is registered as a cure for gray mold, was considered a positive control, and 0.5% DMSO was the blank control. The Petri dishes were sealed with parafilm and incubated in the dark at 23 ± 1 ℃ for 5 d, and each concentration was performed three times. Mycelial radial growth was measured, and the antifungal activity was expressed as the effective concentration for 50% of the maximal effect (EC50). The relative inhibition of mycelial growth was determined using the following formula 1: Mycelialgrowthinhibition(%)=(C-T)/C×100 where C and T are the average diameters (mm) of B. cinerea mycelia in the blank control and the treatment, respectively. The linear regression equation (Y = a + bx) and the coefficient (R2) were estimated by SPSS 22.0 statistics software and the EC50 value was the logarithm value of x when Y = 5.
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9

Cardiac Events and Echocardiography Correlation

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The statistical analyses were conducted, including continuous variables expressed as mean ± standard deviation (SD) with comparison by using Student’s t-test and categorical variables expressed as percentages with comparison by using the Chi-squared test or Fisher’s exact test. We focused on the analyses of the correlation between cardiac events and results of echocardiography, patient’s history of heart disease, ASA classification, and mortality. Statistical analyses were performed by using SPSS 22.0 statistics software and considered p value less than 0.05 as statistically significant.
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10

Statistical Analysis of Experimental Data

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Data analysis was carried out with Origin 8.5 software (OriginLab Corporation, MA, USA). The data were analyzed by ANOVA and Duncan’s multiple-range test using SPSS 22.0 statistics software (SPSS Inc., Chicago, IL, USA). Statistical significance for differences was tested at 5 % probability level (P < 0.05).
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