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Spss statistics software v 17

Manufactured by IBM
Sourced in United States

SPSS Statistics software V.17.0 is a comprehensive statistical analysis package designed to analyze and understand data. It provides a wide range of statistical techniques and tools to help users interpret complex datasets, identify patterns, and make informed decisions. The software is suitable for use in various industries and research fields.

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7 protocols using spss statistics software v 17

1

Comparative Analysis of Cell Viability

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All data are presented as the mean ± standard error of the mean (SEM), unless otherwise noted. Statistical and graphical analyses were carried out using GraphPad Prism 5 (GraphPad Software, Inc., La Jolla, CA) or SPSS (IBM SPSS Statistics Software v. 17.0, Armonk, NY). The Student’s t-test was used to compare two groups. One-way analysis of variance (ANOVA) with paired samples t-test was used for post hoc determination of statistical differences between three or more groups across a single independent variable. When two classes of variables were compared, two-way ANOVA was applied with independent t-tests. Significance was designated at p<0.05.
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2

Genetic Variants Association Analysis

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Clinical data were assessed using the t-test with SPSS Statistics software v. 17.0 (IBM Corporation, Armonk, NY, USA). The Hardy-Weinberg equilibrium (HWE) assumption was determined online using SHEsis Software (http://analysis.bio-x.cn) for each SNP in cases and controls separately. Allele frequencies were compared using chi-square analyses and Fisher’s exact test (two-sided). SNP genotype frequencies and dominant/recessive model analysis of cases and controls were calculated by logistic regression analysis. P values <0.05 were considered to be statistically significant. Haplotype analysis was conducted using SHEs is Software (http://analysis.bio-x.cn/SHEsisMain.htm) [21 ,22 (link)]. Linkage disequilibrium analysis was examined using Haploview Software (http://www.broadinstitute.org/haploview/).
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3

Statistical Analysis of Categorical and Continuous Variables

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Statistical analyses were performed with SPSS statistics software V.17.0 (SPSS Inc, Chicago, Illinois, USA). Comparisons for categorical variables were performed using Pearson's χ2 test or Fisher's exact test where appropriate. Student t test was used for continuous variables. A two-tailed p value was used for all analyses and values less than 0.05 were considered to be significant.
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4

Characterization of Amygdalin in Processed Bitter Almonds

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Results for relative contents are expressed as the mean of three measurements ± standard deviation (SD).The statistical analyses were performed using SPSS statistics software (v. 17.0, SPSS, Inc., Chicago, IL, USA). Significant differences of three forms of amygdalin relative concentrations among various processed bitter almonds were determined using one-way ANOVA followed by the Duncan’s multiple-range test at p < 0.05. Multivariate statistical analysis of HPLC data was carried out using SIMCA software 13.0 (Umetrics, Umeå, Sweden). Principal component analysis (PCA) was carried out to compare the holistic quality and to find characteristic components of raw, scalded and stir-fried bitter almonds.
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5

Statistical Analysis of Quantitative Data

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SPSS statistics software v17.0 was used for data analyses. Quantitative data are expressed as the mean ± standard deviation (SD).
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6

Statistical Analysis of Experimental Data

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Analyses were conducted using the SPSS Statistics software (v.17.0) program. A Chi-square test (χ2) or Fisher’s exact test was used for the data enumeration. Normally distributed variables were expressed as the mean ± the standard deviation (SD) and were compared with independent sample t-tests. One-way analysis of variance was used to compare the differences between the three groups. The Least-Significant Difference (LSD) test or Dunnett’s T3 test was used for pairwise comparison between groups. Linear regression analysis and binary logistic analysis were used to study the association between the FSs (dependent variable) and the detection factors (independent variables). All P-values less than 0.05 were considered statistically significant.
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7

Statistical Analysis for Survival Rates

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All samples were used for statistical analysis. The difference between two groups was analyzed by Student’s t test; *p < 0.05, **p < 0.01, ***p < 0.005. Overall survivals were estimated by means of the Kaplan–Meier method and compared using the log-rank test. Univariate analyses were performed using the Cox risk proportion model. Hazard ratio (HR) with 95% confidence interval was used to study the survival rate. Statistical analyses were performed using the SPSS Statistics software V 17.0 (SPSS Inc., Chicago, IL, USA). The p-values, which was smaller than 0.05, were considered as statistically significant. Center value was defined as mean value, and S.E.M. was used to calculate and plot error bars from raw data. In addition, the Pearson Correlation analysis was used in this study; the r < 0 is negative correlation and r > 0 is positive correlation.
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