The statistical analysis was performed using SPSS, version 17.0 for Windows (SPSS, Inc., Chicago, IL, USA). The normality of the data was determined using Kolmogorov-Smirnov normality tests. The data were expressed as the mean ± standard deviation for normally distributed data and as median and interquartile ranges for non-normally distributed data. For normally distributed data, the variables were compared using Student’s t test for two groups. For skewed data, the Mann-Whitney U test was used. Qualitative or categorical variables were described as frequencies and proportions. Proportional variables were compared using the χ2 test or Fisher’s exact test. Logistic regression was adopted to assess the factors that were significant for predicting outcomes after 2 weeks of treatment. All statistical tests were two-tailed and were performed at a significance level of P < 0.05.
Spss version 17.0 for windows
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SPSS version 17.0 for Windows is a statistical software package that provides advanced analytical capabilities. It offers a range of data management, analysis, and reporting tools to help users gain insights from their data. The software supports a variety of data formats and provides a user-friendly interface for conducting statistical analyses.
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Sas 9, by SAS Institute (1 mentions)
Origin 8, by OriginLab (1 mentions)
R statistical software v 3, by R Project for Statistical Computing (1 mentions)
Prism version 4, by GraphPad (1 mentions)
Progenesis qi software, by Waters Corporation (1 mentions)
Isopropyl myristate, by Merck Group (1 mentions)
Jojoba oil, by Merck Group (1 mentions)
Cellulose membrane, by Merck Group (1 mentions)
Hematoxylin, by Merck Group (1 mentions)
Rhodamine b, by Merck Group (1 mentions)
Cremophor el, by Merck Group (1 mentions)
Caproyl 90, by Gattefosse (1 mentions)
Xylene, by Merck Group (1 mentions)
Transcutol hp, by Gattefosse (1 mentions)
152 protocols using spss version 17.0 for windows
1
Statistical Analysis of Treatment Outcomes
2
Mechanical Ventilation and Retroperitoneal Lacuna
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The statistical analyses were carried out with the SPSS version 17.0 for Windows (SPSS Inc., USA). All the variables were expressed as mean ± SD. Linear correlation and regression were performed to determine the correlation between amount of CO2 which is eliminated from body via mechanical ventilation and the area of retroperitoneal lacuna and obtain the equation of linear regression. Significance was set at P < 0.05.
3
Predictors of Depression in Motor Disability
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All analyses were performed using SPSS version 17.0 for Windows (SPSS Inc.). Data were screened for outliers and missing values. No transformations were judged necessary and all variables met criteria for the assumptions of multivariate analysis [38] .
Hierarchical multiple regression analyses were planned to determine the proportion of variance in the current depression (BDI-II) accounted for by psychological variables, in addition to that explained by the planned covariate of motor disability (PADLS). In order to reduce the number of independent variables entered into the main regression model, subscales of the IPQ-R and CBSQ were entered into separate multiple regression analyses to identify the strongest predictors of depression symptom severity, as measured by the BDI-II (dependent variable). Given the multiple variables, alpha level of the final model was set conservatively to 0.01.
Separately, logistic regression was planned to determine the strongest predictors of depression status (BDI-II score ≥ 14). Predictors were transformed into dichotomous variables, using their median as the cut-off score. Although less sensitive than using continuous scores, the findings provide accessible summaries of potential important predictors of outcome, even if confidence intervals tend to be large.
Hierarchical multiple regression analyses were planned to determine the proportion of variance in the current depression (BDI-II) accounted for by psychological variables, in addition to that explained by the planned covariate of motor disability (PADLS). In order to reduce the number of independent variables entered into the main regression model, subscales of the IPQ-R and CBSQ were entered into separate multiple regression analyses to identify the strongest predictors of depression symptom severity, as measured by the BDI-II (dependent variable). Given the multiple variables, alpha level of the final model was set conservatively to 0.01.
Separately, logistic regression was planned to determine the strongest predictors of depression status (BDI-II score ≥ 14). Predictors were transformed into dichotomous variables, using their median as the cut-off score. Although less sensitive than using continuous scores, the findings provide accessible summaries of potential important predictors of outcome, even if confidence intervals tend to be large.
4
Post-PCI Mortality and MACE Risks
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This was a cross-sectional study to determine the incidence and risk of mortality and MACE based on the NMCRS stratification in post-PCI patients admitted to the ICCU of CMH. We drew upon secondary data obtained from the medical records of patients who underwent PCI in the ICCU of CMH. Data were taken from patients admitted between August 1st, 2013, and August 31st, 2014. The target population and research subjects comprised patients with the acute coronary syndrome that underwent PCI in the ICCU of CMH. Only patients with complete NMRCS variables stated in the medical records of the ICCU of CMH were included in the study, and patients with incomplete data were excluded. For the statistical analyses, the statistical software SPSS version 17.0 for Windows (SPSS Inc., Chicago, IL) was used. The data are described for the categorical variables in percentages, and the numerical data are presented in means ± SDs. The study protocol was approved by the Research Legal and Ethics Committee of CMH and the Faculty of Medicine, Universitas Indonesia.
5
Validation of Pedometer Step Counts
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The SPSS version 17.0 for Windows (SPSS, Inc., Chicago, IL) was used for statistical analysis. Data from a total of 30 subjects were used in our analysis. Step counts were measured for both sexes and for different categories of BMI status. The relationship and agreement between the results of 2 devices were assessed using Spearman correlation and Bland–Altman analysis. Bland–Altman plots were constructed to show the level of agreement between the pedometers and the criterion measure.
Wilcoxon test was used to compare the mean step scores of 2 pedometers and to determine whether there was a significant difference in the mean scores of 2 pedometers at both sexes and different BMI groups. For all statistical analyses, α level of 0.05 was used to show significant differences, and all values are shown as mean ± SD.
Wilcoxon test was used to compare the mean step scores of 2 pedometers and to determine whether there was a significant difference in the mean scores of 2 pedometers at both sexes and different BMI groups. For all statistical analyses, α level of 0.05 was used to show significant differences, and all values are shown as mean ± SD.
6
Analyzing Clinicopathological Factors and Survival Outcomes
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The results are presented as the mean ± SD and were analyzed using SPSS version 17.0 for Windows (SPSS, Inc.). The unpaired Student's t-test or Mann-Whitney U test were conducted to compare the two groups. One-way analysis of variance (ANOVA) or two-way ANOVA with Bonferroni's post hoc test were also used when comparing more than two groups. To determine the associations between NRCAM and clinicopathological features, Fisher's exact test or Pearson χ2 test were performed. Kaplan-Meier method and Cox proportional hazards regression model were conducted for survival estimation. P<0.05 was considered to indicate a statistically significant difference.
7
Statistical Analysis of Experimental Data
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All statistical analyses were performed using the SPSS Version 17.0 for Windows (significance was established at P < 0.05). Data were expressed as mean ± SD (standard deviation), statistical significance was evaluated by one-way analysis of variance (ANOVA) combined with Duncan’s multiple range tests. All experiments were performed in triplicate, unless otherwise indicated.
8
Evaluating Osteotomy Outcomes: Descriptive Analysis
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A descriptive analysis was carried out (means, standard deviations, quartiles, maximum and minimum values) for each variable. For qualitative variables, the absolute and relative frequencies were calculated. A paired t test was used to compare MME preoperatively and postoperatively.2 (link) The Pearson correlation coefficient was used to measure the degree of relationship between scores.2 (link) The statistical analysis was carried out using SPSS Version 17.0 for Windows (SPSS Inc). Considering the reduction of MME after HTO as the main analyzed outcome, for a power analysis of 80% with a 5% level of significance, a patient sample size of 62 was needed.
9
Upper Limb Dominance Influences on Rehabilitation
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Database management and statistical analyses were performed by an independent
researcher, who was blinded to the group allocations. All measures were analyzed with
intention-to-treat analyses, and descriptive statistics were calculated for all
outcome measures. Analyses of covariance (ANCOVA), which controlled for the baseline
characteristics, were employed to analyze the effects of the intervention. The
results were reported as means and standard deviations or means and 95% confidence
intervals (CI). Repeated-measures ANOVA, followed by pre-planned contrasts, were used
to verify the main and interaction effects within and between groups for the four
time points. To better understand the influences of upper limb dominance on the
acquisition and maintenance of the improvements, the differences between the groups
were provided as means and 95% CI. This type of analysis was chosen because, while
the null hypothesis significance tests use probability levels (e.g. p<0.5), effect
size analyses focus on the magnitude of the differences between the groups and the
probability of an effect to report and interpret the results. This type of
description assists in determining the clinical interpretation and importance of the
observed differences, as well as the statistical significance of the findings26 (link)
,27 (link). All analyses were performed with SPSS,
version 17.0 for Windows.
researcher, who was blinded to the group allocations. All measures were analyzed with
intention-to-treat analyses, and descriptive statistics were calculated for all
outcome measures. Analyses of covariance (ANCOVA), which controlled for the baseline
characteristics, were employed to analyze the effects of the intervention. The
results were reported as means and standard deviations or means and 95% confidence
intervals (CI). Repeated-measures ANOVA, followed by pre-planned contrasts, were used
to verify the main and interaction effects within and between groups for the four
time points. To better understand the influences of upper limb dominance on the
acquisition and maintenance of the improvements, the differences between the groups
were provided as means and 95% CI. This type of analysis was chosen because, while
the null hypothesis significance tests use probability levels (e.g. p<0.5), effect
size analyses focus on the magnitude of the differences between the groups and the
probability of an effect to report and interpret the results. This type of
description assists in determining the clinical interpretation and importance of the
observed differences, as well as the statistical significance of the findings26 (link)
,27 (link). All analyses were performed with SPSS,
version 17.0 for Windows.
10
Linkage Analysis and QTL Mapping for Wheat
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SPSS version 17.0 for Windows (SPSS Inc., Chicago, IL) was used for basic statistical analyses and graph production. JointMap 4 [31] was used for linkage analysis of marker data. A minimum limit of detection (LOD) of ≧3.0 was used to develop the linkage map. Recombination frequencies were converted to centimorgans (cM) by using the Kosambi mapping function. The segregation ratio of each marker and its deviation from the expected ratio were evaluated using the chi-square test. QTL analysis was conducted using QTL IciMapping version 3.2 [32] following inclusive composite interval mapping (ICIM) [33] . An LOD score of ≧2.5 was used to detect a QTL, and the walk speed was 1.0 cM. The QTLNetwork program version 2.1 based on a mixed linear model [34] (link) was used to identify the epistatic QTL for brittle rachis and threshability in joint analysis of the phenotypic values for the data collected over two years. Briefly, the values for testing window and filtration window were set at 10 cM, and the walking speed was 1 cM. The LOD threshold of QTL was determined using a 1,000 permutation test at 95% confidence level. The proportion of observed phenotypic variance explained by each additive and epistatic QTL and the corresponding additive effects were also estimated.
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