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Spss 17.0 statistical package for windows

Manufactured by IBM
Sourced in United States

SPSS 17.0 is a statistical software package for Windows that provides a range of data analysis and statistical modeling tools. The core function of SPSS 17.0 is to enable users to analyze and interpret data, as well as to generate reports and visualizations.

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Lab products found in correlation

3 protocols using spss 17.0 statistical package for windows

1

Triplicate Analyses of Treatment Effects

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All experiences and statistical analyses were made in triplicate. All results are expressed as the mean standard deviation. Statistical analysis was performed with the SPSS 17.0 statistical package for Windows (SPSS, Inc., Chicago, IL, USA). A two-way ANOVA followed by Tukey’s post hoc test was performed to compare treatment and control groups. Statistical significance was set at p = 0.05.
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2

Tuber Yield Analysis Protocol

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Data on yield (length and weight of tubers) were subjected to analysis of variance (ANOVA) test using a completely randomized design (CRD). Means were separated by least significant difference (LSD) test at the 5% probability level. All computations were performed using SPSS 17.0 statistical package for Windows (SPSS, Inc., Chicago, IL, USA).
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3

Evaluating Inflammatory Markers in RMVS

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Statistical analysis was performed with SPSS 17.0 Statistical Package for Windows (SPSS Inc, Chicago, Illinois, USA). Continuous variables were given as the median±standard deviation, and categorical variables were defined as percentages. Data were tested for normal distribution using the Kolmogorov- Smirnov test. Categorical variables were compared with chi-square test. Student t-test or Mann-Whitney U test was used to compare continuous variables. Mean values were compared by ANOVA, followed by Tukey HSD test among different groups. Pearson’s correlation analysis was performed to define the correlation between hs-CRP levels and the (NLR). The receiver operating characteristics curve was used to demonstrate the sensitivity and specificity of NLR and the optimal cut-off value for predicting RMVS. In order to define the relationship between RMVS and possible confounding factors, univariate and multiple logistic regression analysis was performed. A p value of <0.05 was accepted as statistically significant.
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