Spss 20.0 for windows

Manufactured by IBM

Sourced in United States, Japan

SPSS 20.0 for Windows is a comprehensive software package designed for statistical analysis. It provides a wide range of tools and techniques for data management, analysis, and presentation. The software is used in various fields, including business, academia, and research, to help users gain insights and make data-driven decisions.

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Lab products found in correlation

Prism 8, by GraphPad (5 mentions) Prism 6, by GraphPad (5 mentions) Prism 5, by GraphPad (4 mentions) Spss version 20, by IBM (3 mentions) Matlab based custom software, by MathWorks (2 mentions) Windows, by MedCalc (2 mentions) Statistical packages for the social sciences, by IBM (1 mentions) Andor ixon charge coupled device imaging apparatus, by Oxford Instruments (1 mentions) Sigmaplot 11, by Grafiti LLC (1 mentions) Originpro 8, by OriginLab (1 mentions) Calcusyn software, by Biosoft (1 mentions) Pls toolbox 8, by Eigenvector Research (1 mentions) Software, by MedCalc (1 mentions) Originpro 2021b, by Adobe (1 mentions) Origin 8, by OriginLab (1 mentions) Coreldraw x5, by Corel (1 mentions) Specific antisera, by Denka Seiken (1 mentions) Origin 2017, by OriginLab (1 mentions) Illustrator cs6, by Adobe (1 mentions) Matlab, by MathWorks (1 mentions)

662 protocols using spss 20.0 for windows

Exponential Decay Model for Alfalfa Silage

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An exponential decay model was used to fit the WSC and monosaccharide (glucose, fructose, and xylose) data using IBM Statistical Packages for the Social Sciences (IBM SPSS 20.0 for Windows) to describe the sugar change characteristics with the following equation:

y = y 0 + a * exp (- b * x)

where y (g/kg DM) is the residue at any time x (day), y0 (g/kg DM) is the total residual fraction after 60 days of ensiling, a (g/kg DM) is the consumable fraction, b (day⁻¹) is the fractional consumption rate of a and x is the ensilage time (day) [4 (link)].
The statistical analyses were performed using the IBM Statistical Packages for the Social Sciences (IBM SPSS 20.0 for Windows). The data were analysed by two-way analysis of variance (ANOVA, general linear models) (five treatments × four ensiling time × four replicates) to evaluate the effects of additives, ensiling time and their interaction on the fermentation characteristics, structural carbohydrate degradability and nonstructural carbohydrate fermentation of alfalfa silages. The data were analysed by one-way ANOVA (five treatments × four replicates) to evaluate the effects of additives on DM, DM recovery, CP and microbial composition of alfalfa silages after ensiling for 60 days. The means were then compared for significance using Tukey’s test at P < 0.05.

Liu Q., Li J., Zhao J., Wu J, & Shao T. (2019). Enhancement of lignocellulosic degradation in high-moisture alfalfa via anaerobic bioprocess of engineered Lactococcus lactis with the function of secreting cellulase. Biotechnology for Biofuels, 12, 88.

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Analyzing Day 1 and Day 5 Differences

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Differences between day 1 and day 5 were analyzed by Mann–Whitney–Wilcoxon rank-sum test (SPSS 20.0 for Windows). Linear regression analyses were performed for analyses of association (SPSS 20.0 for Windows). Results are expressed as mean (median) ± sem.
A p-value<0.05 was deemed significant.

Sepp D., Franz D., Triftshaeuser N., Ott I., Esposito-Bauer L., Feurer R., Seifert C.L., Thaler M., Hemmer B, & Poppert H. (2014). Mobilization of CD133+ Progenitor Cells in Patients with Acute Cerebral Infarction. PLoS ONE, 9(3), e70796.

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Chemical Composition of Pu-erh Tea

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Each pu‐erh tea sample was measured in three replications. All data are presented as mean value ± standard deviation (SD). The independent t test using SPSS 20.0 for Windows was carried out to determine whether the significant difference at p < .05 level or the highly significant difference at p < .01 level exists between JPT and GPT (control). Bivariate correlation analysis using SPSS 20.0 for Windows was carried out to determine effects of each dominant fungus on chemical composition after a long‐term storage.

Zhou B., Ma C., Ren X., Xia T., Zheng C, & Liu X. (2020). Correlation analysis between filamentous fungi and chemical compositions in a pu‐erh type tea after a long‐term storage. Food Science & Nutrition, 8(5), 2501-2511.

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Statistical Analysis of Experimental Data

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One-way and two-factor ANOVA with Duncan’s test at a p level of 0.05 were performed using SPSS 20.0 for Windows (SPSS Inc., Chicago, IL, USA). Pearson’s correlation analysis was also carried out using SPSS 20.0 for Windows. The heat maps were obtained using the ‘pheatmap’ package in R (3.1.0). The bar and line graphs were plotted using Origin 8.0 software (OriginLab, Northampton, MA, USA).

Qian X., Sun L., Xu X.Q., Zhu B.Q, & Xu H.Y. (2017). Differential Expression of VvLOXA Diversifies C6 Volatile Profiles in Some Vitis vinifera Table Grape Cultivars. International Journal of Molecular Sciences, 18(12), 2705.

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Tea Plantation Characterization: Linking Leaves, Soil, and Microbes

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The raw data of tea-leaves quality components, soil environmental factors and microbial community composition in modern and ancient tea plantations of five sampling sites are present in Additional file 1. The independent-samples T-test was carried out to compare tea-leaves chemical components, soil chemicophysical properties, and microbial community structure between ancient and modern tea plantations using SPSS 20.0 for Windows (IBM, Armonk, NY, USA). Principal coordinates analysis (PCoA) and non-metric multidimensional scaling (NMDS) were employed to evaluate the differences of soil microbial community composition according to Bray-Curtis distances between two types of tea plantations using Canoco 4.5 software (Microcomputer Power, Ithaca, NY, USA). The interactions among tea-leaves chemical components, soil environmental factors and microbial community composition in tea plantation were confirmed through the bivariate correlation analysis of SPSS 20.0 for Windows (IBM, Armonk, NY, USA) and the redundancy analysis (RDA) of Origin 9.0 software (OriginLab, Hampton, MA, USA), respectively.

Yang G., Zhou D., Wan R., Wang C., Xie J., Ma C, & Li Y. (2022). HPLC and high-throughput sequencing revealed higher tea-leaves quality, soil fertility and microbial community diversity in ancient tea plantations: compared with modern tea plantations. BMC Plant Biology, 22, 239.

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Statistical Analysis of Renal Transplant Outcomes

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Kolmogorov–Smirnov tests were used to assess distributions. The clinical characteristics of renal transplant recipients were expressed as medians (quartile 1–quartile 3) or numbers. Kruskal–Wallis tests or Mann–Whitney U tests were used to elucidate differences between groups. Spearman’s rank correlation coefficient test was used to assess correlations in continuous values between groups, and all results were expressed as correlation coefficients (r values). The effects of factors in univariate analysis were evaluated using stepwise multiple linear regression analysis. Variables with borderline significance (p < 0.2) on the univariate analysis were subjected to multivariate regression analyses. Dummy variables were used to replace the groups (1 and 0 in 2 groups; 1 and 0, 0 and 0, and 0 and 1 in 3 groups). Results with p values of less than 0.05 were considered significant, and SPSS 20.0 for Windows (SPSS IBM Japan Inc., Tokyo, Japan) was used for all statistical analyses.

Yagishita H., Kagaya H., Saito M., Numakura K., Yamamoto R., Sagehashi R., Habuchi T., Satoh S, & Miura M. (2022). Effects of NR1I2 and ABCB1 Genetic Polymorphisms on Everolimus Pharmacokinetics in Japanese Renal Transplant Patients. International Journal of Molecular Sciences, 23(19), 11742.

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Statistical Analysis of Experimental Findings

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One-way analysis of variance (ANOVA) test with a post hoc Scheffe’s test was used to evaluate the results for statistical significance with the software SPSS 20.0 for Windows (IBM SPSS, Chicago, IL, USA). A p-value lower than 0.05 was indicative of statistical significance.
The scheme of the experimental process can be seen in Figure 2.

Lopez de Armentia S., Abenojar J., Ballesteros Y., del Real J.C., Dunne N, & Paz E. (2022). Polymerization Kinetics of Acrylic Photopolymer Loaded with Graphene-Based Nanomaterials for Additive Manufacturing. Nanomaterials, 12(24), 4498.

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Comparative Analysis of Experimental Methods

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Each property was expressed as mean ± standard deviation. The results were also evaluated for statistical significance using a one-way analysis of variance (ANOVA) test with a post-hoc Scheffe’s test (SPSS 20.0 for Windows; IBM SPSS, Chicago, IL, USA). A p-value less than 0.05 was indicative of statistical significance.

Paz E., Ballesteros Y., Abenojar J., Dunne N, & del Real J.C. (2021). Advanced G-MPS-PMMA Bone Cements: Influence of Graphene Silanisation on Fatigue Performance, Thermal Properties and Biocompatibility. Nanomaterials, 11(1), 139.

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Comparative Analysis of Study Outcomes

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Descriptive analyses were used to describe the study sample; Chi-square and/or t-test analysis was used to compare the socio-demographic characteristics of the study population. Differences in demographic characteristics were deduced using the χ² test for categorical variables, and nonparametric Wilcoxon test was applied for continuous variables. Paired sample Student’s t-test was used to compare the baseline and week 24 within the intervention group and the control group. The independent Student’s t-test was used to compare differences between the intervention and control groups. The mean ± SD difference between the baseline and week 24 for all variables was also calculated. The Pearson correlation coefficient was leaned on to detect associations between the study variables. All statistical analyses were performed using IBM SPSS 20.0 for Windows (SPSS Inc., Chicago, IL, USA). All the statistical tests were based on two-tailed hypothesis tests, where the significance level was defined as p < 0.05.

Pavithran N., Kumar H., Menon A.S., Pillai G.K., Sundaram K.R, & Ojo O. (2020). South Indian Cuisine with Low Glycemic Index Ingredients Reduces Cardiovascular Risk Factors in Subjects with Type 2 Diabetes. International Journal of Environmental Research and Public Health, 17(17), 6232.

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Cerebral Lactate and Intracranial Pressure

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All statistical analyses were performed using SPSS 20.0 for Windows (IBM SPSS, USA). Data is expressed as mean±standard deviation (×±SD). ANOVA and Student’s unpaired t-tests were used to evaluate significant differences. Spearman’s correlation was used to analyze the correlation between cerebral lactate and ICP. A p-value of <0.05 was considered statistically significant.

Tang Z., Liang M., Chen G., Rong J., Yao J., Chen Z., Yang X, & Wu Z. (2018). Neuroprotective effect of selective antegrade cerebral perfusion during prolonged deep hypothermic circulatory arrest: Cerebral metabolism evidence in a pig model. Anatolian Journal of Cardiology, 19(1), 2-10.

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