A spreadsheet was built in Excel® Office containing information on identification, general characteristics, questionnaire data on pregnant women, anthropometric data, and laboratory test results. The spreadsheet was revised, consolidated, and imported in the SPSS 24.0 statistical package (IBM®). Categorical variables were shown as absolute and percentage numbers and compared by the Chi-square test. Continuous variables were tested for their normality, shown as mean (standard error) and compared using Student's t-test. The linear regression Enter method was used for multivariate analysis. Statistical significance is claimed for p < 0.05.
Spss 24.0 statistical
Manufactured by IBM
Sourced in United States
SPSS 24.0 is a statistical software package designed for data analysis. It provides a comprehensive set of tools for data management, analysis, and visualization. The software is widely used in various fields, including social sciences, market research, and data-driven decision-making.
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Lab products found in correlation
71 protocols using spss 24.0 statistical
1
Maternal Data Analysis Protocol
2
Analysis of Inpatient Service Utilization
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Epidata 3.0 was employed to input data, and the database was imported into Excel and transferred to SPSS 24.0 statistical software (IBM Corporation, Chicago, IL, USA,) for analysis. The differences between variables were compared using the χ2 statistic method after the complex sampling was weighted. The complex sampling logistics regression was used in multi-factor analysis with the test standard set as α = 0.05 and to analyze 3 index factors in the utilization of inpatient services. Crude odds ratio (cOR) refers to the result obtained by putting variables into the equation separately for analysis. Adjust odds ratio (aOR) refers to the result of putting the independent variables into thee equations together for analysis [24 (link),25 (link)].
3
Glucose and Insulin Response in OGTT
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Data are presented as means ± SD. All statistical tests were performed using SPSS 24.0 statistical software (IBM Analytics, New York, USA). Data was checked for normal distribution using the Shapiro-Wilk test. All data was normally distributed and parametric tests were performed. A one-way analysis of variance (ANOVA) was used to compare means for the three trials, with post-hoc Tukey tests for multiple comparisons between groups. A 3 × 5 repeated measures ANOVA was also used to analyse the insulin and glucose response during the OGTT. If a significant effect was found, post-hoc analysis was performed to identify where significance exists between trials. If a main effect was observed when comparing two trials, paired sample t-tests were used to compare trials at each time-point. Significance was accepted at P<0.05.
4
Anxiety and Depression in Cancer Patients
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All statistical analyses were performed using SPSS 24.0 statistical software (IBM, Chicago, IL, USA), and all figures were plotted using GraphPad Prism 7.00 software (GraphPad Software Inc, San Diego, California, USA). Continuous variables were presented as mean ± standard deviation (SD), and categorical variables were presented as count (percentage). Comparisons of HADS-A score or HADS-D score among groups were determined by one-way analysis of variance (ANOVA) followed by the Bonferroni t test. Comparisons of anxiety or depression prevalence between groups were determined by Chi-Squared test. Comparisons of anxiety or depression severity between groups were determined by Wilcoxon rank sum test. Correlation of anxiety or depression with clinical characteristics was determined by Chi-Squared test or Wilcoxon rank sum test. OS was displayed with Kaplan–Meier curve. The difference of OS in subgroups was determined by log-rank test. P value < .05 was considered significant.
5
Descriptive Statistics of Survey Responses
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Descriptive statistics were used to describe survey responses. The overall and subscale score distributions were assessed by discipline, and between group differences were tested with the ANOVA test. All p‐values are two‐sided with values ≤.05 indicating statistical significance. Statistical tests were done using the SPSS 24.0 statistical software (IBM Corp).
6
Evaluation of Experimental Treatments
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All experimental data were presented as the mean ± standard deviation and analyzed three times at least with SPSS 24.0 statistical software (IBM, Armonk, NY, United States). One-way analysis and repeated measures analysis of variance was used to compare differences among the groups. For the further comparison of differences between the two groups, the post-test, the least-significant difference test, was used. It was considered that a value of p < 0.05 has statistically significant.
7
Statistical Analysis of Experimental Data
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Measured data presented as means ± standard deviations were analyzed using SPSS 24.0 statistical software (IBM, Armonk, NY, USA). Data obeying normal distribution and homogeneity of variance between two groups were compared using either paired t test or unpaired t test. Comparisons among multiple groups were analyzed using one-way analysis of variance (ANOVA), followed by Tukey’s tests with corrections for multiple comparisons. Statistical analysis in relation to time-based measurements within each group was realized using ANOVA of repeated measurements, followed by a Bonferroni post hoc test for multiple comparisons. Pearson correlation analysis was used to analyze the correlation between CLCA4 and miR-590-3p expression. Two-way ANOVA and the Sidak post hoc test were used to identify the differences among groups. A value of p <0.05 was statistically significant.
8
Prone Positioning and Oxygenation Improvement
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Data entry was completed using Microsoft Excel while data analysis was completed using SPSS 24.0 statistical software (IBM Corp., Armonk, NY, USA). Measures with normal or approximately normal distributions are expressed as the mean ± standard deviation (SD). Categorical data are expressed as frequencies and percentages. The clinical data of the patients among the different groups were compared using the t-test, chi-squared test, analysis of variance (ANOVA), and nonparametric test. Pearson correlation analysis was used to analyze the correlations between the variables, and multiple linear regression analysis was performed to determine the factors contributing to the duration of prone position. Logistic regression analysis was used to determine the associated factors of the initial improvement in SpO2 after prone positioning.
9
Exploring Depression, Smoking, and Drinking Impacts on Periodontal Health
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Considering the complicated sample design, all analyses incorporated the NHANES sampling weights to ensure the representativeness of noninstitutionalized civilian resident population of the United States. Descriptive data were presented as mean (standard deviation) for continuous variables and number (percentage) for categorical variables in terms of sociodemographic and periodontal characteristics. The multivariate logistic regression model was used to analyze the independent effects and multiplicative interaction between depression, smoking, and drinking based on initial model and adjusted model (adjusted for age, gender, ethnicity, education, household income and history of diabetes). We analyzed the interaction effect across different gender (male and female) and age groups (≤ 45 and > 45) [27 (link)]. Odds ratio (OR) and 95% confidence interval (CI) were calculated. All statistical analyses were performed using the SPSS 24.0 statistical software package (IBM SPSS Statistics for Windows, Version 24.0) and R (versionversion 4.3.1). The statistical significance was set at P < 0.05 using the two-sided test.
10
Investigating Protein Expression Dynamics
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The results are expressed as the median and interquartile range. The statistical significance of the differences observed in the experimental groups compared to the control group was analyzed using a Mann-Whitney U-test on SPSS 24.0 statistical software (IBM Corp., Armonk, NY, USA). A p-value less than 0.05 was considered significant.
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