The Statistical Package for Social Sciences (SPSS) for windows v:15.0 (IBM Corp., Armonk, NY, USA) software was used to perform statistical analysis. To determine whether the variables had normally distributed or not, histograms and Shapiro–Wilk test were performed. Categorical variables were given as frequencies (n) and percentage (%). Continuous numerical parameters were compared between two groups by using Student’s t or Mann–Whitney U tests. Comparison of categorical parameters was performed with Chi-square test. The relationships were assessed with Pearson’s correlation analysis test for normally distributed variables and with Spearman’s correlation analysis for not normally distributed variables. A value of p < 0.05 was accepted as significant.
Spss v 15 for windows
Manufactured by IBM
Sourced in United States
SPSS v.15 for Windows is a statistical software package developed by IBM. It provides a range of data analysis and reporting capabilities, including data management, statistical analysis, and visualization tools. The software is designed to work with Windows operating systems.
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Lab products found in correlation
7 protocols using spss v 15 for windows
1
Statistical Analysis in Social Sciences
2
Strength Training Effects on Fitness
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Unpaired sample t-tests were run for each variable to determine whether there were any initial between-group differences for the premeasurements. On completion of the training period, paired sample t-tests were then run for each variable to determine within-group significant differences from pre-to postmeasurements of 1RM, SBJ, and body mass. A mixed analysis of variance (ANOVA) (SPSS for Windows, V. 15.0; IBM Corp., Armock, NY, USA) was used to determine whether main effects or an interaction were found and if so, to compare the significant differences between the 2 groups. A p value of 0.05 was used to establish the criteria for statistical significance. Effect sizes for the 1RM were calculated using the formula Cohen's d = (M2-M1)/SD pooled ( 26 ). Unpaired t-tests were implemented to determine whether there was a training load difference between groups during the last week of training.
3
Evaluating Weight Management Interventions
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SPSS v.15 for Windows was used for data processing and analysis. A statistically significant change was defined as p < 0.05. Differences in general demographic information between the two groups were measured using the chi-squared test. An independent-sample t-test was performed to compare the BMI, WHR, BFR, and RSE score between the two groups at the beginning of the study. Moreover, generalized estimating equations (GEEs) were used to assess the improvements in the BMI, WHR, BFR, and RSE score at the end of week 4 and week 8.
4
Mechanical Testing of Spinal Implants
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The theoretical model of mechanical testing was accomplished using the well-known guidelines of ASTM F1717-04 "Static and Dynamic Test Method for Spinal Implant Assemblies in a Corpectomy Model" to evaluate the mechanical properties of the 6.5 mm rod under static and dynamic loading. Non-destructive load was applied during flexion/extension and torsion testing9 (link)20 (link)22) . In order to apply the load to the corpectomy models, a custom-made system was designed for the test machines. Flexion/extension tests were accomplished using an electromechanical universal testing machine (Shimadzu 5kN AG-X, Japan) (Fig. 1 ). To simulate spine-loading conditions, the samples were subjected to a load of 100 N to ensure an 8.4-Nm moment with a velocity of 5 mm min-1.7 (link)22) To examine torsional loading, the UHMWPE blocks were mounted in a torsion testing machine (TecQuipment, Ltd., United Kingdom) and subjected to rotational displacement of 0.5° s-1 to an end point of 5.0° (Fig. 3 )19 (link)20) (link). The obtained data were analyzed using SPSS v.15. for Windows (SPSS Inc., Chicago, IL, USA). The non-parametric one-way analysis of variance was used for categorical comparisons. Statistical significance level was set at p<0.05.
5
Behavioral Analysis of Injury Models
6
Statistical Analysis of Experimental Data
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Statistical analyses were performed using Prism v.5.0 for Windows (Graph Pad Software
Inc., CA, USA) and SPSS v.15 for Windows (SPSS Inc., Madrid, Spain). Data are
presented as mean ± SEM and differences among means were tested by one-way ANOVA
followed by the Tukey post-test comparing each treatment with control. Statistical
significance was set for p < 0.05.
Inc., CA, USA) and SPSS v.15 for Windows (SPSS Inc., Madrid, Spain). Data are
presented as mean ± SEM and differences among means were tested by one-way ANOVA
followed by the Tukey post-test comparing each treatment with control. Statistical
significance was set for p < 0.05.
7
Unsupervised Multivariate Analysis for Species Discrimination
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Unsupervised multivariate analysis discarding manual intervention was carried out. For this, several canonical discriminant analyses (DAs) at different scales were performed using the software SPSS v. 15 for Windows (SPSS, Chicago, USA) following a systematic, sequential approach. The procedure was unsupervised, assuming some artificial criteria to rule out manual intervention and thus decisions based on previous knowledge. This was done by selecting the variable showing the highest percentage of variance explained in the first discriminant function in each DA. This character was then represented in a box-plot, allowing the separation of the dataset into two subsets. Once the variable was chosen, the threshold for splitting the data was established according to two conditions: (1) the main bodies of the box-plots could not overlap, and (2) the threshold should minimize the number of misclassified cases for each step. This procedure was recurrently applied until every species and subspecies was individually classified. The misclassification rate was calculated as explained in the previous section. The box-plots were generated using the “ggplot2” package in R [72 ].
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