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Spss statistic software version 22

Manufactured by IBM
Sourced in United States

SPSS Statistics Software version 22 is a comprehensive and powerful data analysis tool developed by IBM. It offers a wide range of statistical capabilities to help users analyze and interpret complex data. The software provides a user-friendly interface and a wide selection of statistical procedures to assist in decision-making processes. SPSS Statistics is a widely used tool in various industries, including academia, research, and business.

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Lab products found in correlation

6 protocols using spss statistic software version 22

1

Probit Regression for Analytical Sensitivity

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Descriptive and inferential analyses were performed with SPSS Statistic Software version 22.0 (Armonk, NY, IBM Corp.). Statistical figures were prepared using RStudio Version April 1, 1717 (RStudio Team (2021). RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA, USA; URL http://www.rstudio.com/).
Probit regression analysis was used to calculate LOD for analytical sensitivity. Pearson's Chi-square (χ2) was used to assess statistical differences between qualitative variables (genotype distribution, and genotype with sex, risk factors, location of lesions, simple or multiple lesions and prior treatment), and the t-test was used to calculate mean differences (age and time since onset) with other factor variables. The Kappa (κ) statistic was applied to calculate the level of agreement for the precision (inter and intra-run) assays. All CI were 95 %.
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2

Effect of Compound X on Cell Viability

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All statistics were carried out using IBM SPSS Statistic software version 22.0 (IBM Corp., USA). All analyses were replicated a minimum of three times. Data are expressed as mean ± standard deviation, and comparisons among data were carried out using Student t-test or repeated measures ANOVA followed by Tukey's test. Mean values were considered to significantly differ at p < 0.05.
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3

Statistical Analysis of Experimental Data

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Data collected were divided according to data type. We selected the appropriate statistical method for each dataset and, finally, data analysis was carried out by IBM SPSS statistic software version 22 for parametric data using a one-way ANOVA test and a linear regression test, which is presented in the form of tables and graphics.
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4

Statistical Analysis of Experimental Data

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The results were expressed as the mean ± standard error of mean (SEM) which were calculated from duplicate laboratory test values. Graphs were plotted using GraphPad Software. Differences among each group were investigated using one-way analysis of variance (ANOVA) with post hoc test for least significant difference (LSD) assuming equal variances using the IBM SPSS statistic software version 22 (SPSS: Statistical Package for Social Sciences). Significant difference was accepted at p  <  0.05.
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5

Optimizing Biomass Conversion Efficiency

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The values were shown as the means ± standard deviation (SD) of three replicates of samples. Statistical analysis of data was performed by One-way analysis of variance (ANOVA) followed by Duncan's multiple range tests at significance level of P < 0.05 using IBM SPSS Statistic software version 22.
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6

Statistical Analysis of Experimental Data

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Samples were analyzed in triplicates and data expressed as mean ± Standard error. The data were analyzed by one-way ANOVA with post hoc corrected two-tailed t-tests using the IBM SPSS statistic software version 22 (SPSS: Statistical Package for Social Sciences). Significant differences at P ˂ 0.05 were considered significant.
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