Analysis of the data was conducted with Microsoft Excel 2007 and SPSS 22 statistical software (SPSS, Inc., Chicago, IL, USA). The data were expressed as the mean ± SEM. A “p” value of ≤ 0.05 was considered to be statistically significant. The SNK test was used to analyze the difference between two groups. The receiver’s operating characteristic curve (ROC) was plotted to assess the diagnostic accuracy of patients with severe liver fibrosis compared to healthy individuals. It was also used to distinguish between patients with severe liver fibrosis and those with mild liver fibrosis. The relationship of the true positivity (sensitivity) and the false positivity (1- specificity) was plotted at various cut-off points of the tests. An ideal test was considered to have an AUC of 1.0, with 0.5 indicating no diagnostic value. The interdependence of variables describing relevance was examined by means of the Pearson correlation coefficient (r) and Spearman’s rank correlation coefficient (Spearman’s rho).
Microsoft excel 2007
Manufactured by IBM
Sourced in United States
Microsoft Excel 2007 is a spreadsheet software application developed by Microsoft. Its core function is to organize, analyze, and present numerical data in a grid-based format, allowing users to perform calculations, create charts and graphs, and manage large amounts of information.
Automatically generated - may contain errors
Lab products found in correlation
Spss version 20, by IBM (3 mentions)
Spss 17.0 statistical package, by IBM (1 mentions)
Spss version 18, by IBM (1 mentions)
Spss version 13, by IBM (1 mentions)
Spss for windows version 17, by IBM (1 mentions)
Spss statistics 17, by IBM (1 mentions)
Spss 25, by IBM (1 mentions)
Coreldraw x8, by Corel (1 mentions)
Spss v19, by IBM (1 mentions)
18 protocols using microsoft excel 2007
1
Diagnostic Accuracy of Liver Fibrosis Assessment
2
Genetic Variants and Cancer Risk Analysis
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A chi-squared test was used to evaluate the differences in the distributions of demographic characteristics, selected variables and genotypes between the cases and controls. Continuous variables were analyzed using Student’s t test. Genotype frequencies in the control subjects for each SNP were tested for departure from Hardy-Weinberg equilibrium (HWE) using the goodness-of-fit χ2 test. Polytomous logistic regression analyses were used to estimate the adjusted odds ratios (ORs) and the 95% confidence intervals (CIs) for the association between genetic variants and cancer risk, as evaluated by different genetic models (additive, heterozygous, homozygous, and dominant and recessive) adjusted for age, gender, family history of cancer, cigarette smoking and alcohol drinking status. Benjamini and Hochberg False Discovery Rate (FDR) method was used to adjust the multiple hypothesis tests. Crossover analyses were used to conduct association analyses between rs13042394 and non-cardia GC by cigarette smoking or alcohol drinking status. All of the statistical analyses were performed using Microsoft Excel 2007, SPSS 17.0 statistical package (SPSS, Chicago, IL) and the plink-1.07-dos program. The standard α = 0.05.
3
Data Analysis of Categorical Variables
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Data were entered into Microsoft Excel 2007 and analyzed in SPSS version 18.0 (SPSS Inc., Chicago, USA). All the categorical variables were expressed in terms of number and percentages. Associations between categorical variables were determined using Chi-square or Fisher's exact test. P < 0.05 was considered statistically significant.
4
Neonatal TGA Management Strategies
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We conducted an observational, retrospective, single tertiary-care center study between January 2008 and April 2022, which included 92 neonates with TGA transferred to our institution for specialized treatment. This study excluded only one patient due to a complex diagnosis of the hypoplastic left heart.
Clinical data were retrospectively collected from hospital medical records. From each patient, we collected the following information: gender, gestational age, birth weight, Apgar score, age, time of cardiac diagnosis, comorbidities, echocardiographic measurements, respiratory support, inotropic and vasopressor support, prostaglandin E1, blood gas values, head ultrasound, balloon atrial septostomy technique, and cardiovascular surgical intervention. We divided patients into two groups according to the presence or absence of balloon atrial septostomy procedure.
The processing and statistical interpretation of the obtained data were carried out with the help of software Microsoft Excel 2007, SPSS Statistics 15.0.0 (SPSS Inc., Chicago, IL, USA), and MedCalc 14.8.1 (MedCalc software—2014).
Clinical data were retrospectively collected from hospital medical records. From each patient, we collected the following information: gender, gestational age, birth weight, Apgar score, age, time of cardiac diagnosis, comorbidities, echocardiographic measurements, respiratory support, inotropic and vasopressor support, prostaglandin E1, blood gas values, head ultrasound, balloon atrial septostomy technique, and cardiovascular surgical intervention. We divided patients into two groups according to the presence or absence of balloon atrial septostomy procedure.
The processing and statistical interpretation of the obtained data were carried out with the help of software Microsoft Excel 2007, SPSS Statistics 15.0.0 (SPSS Inc., Chicago, IL, USA), and MedCalc 14.8.1 (MedCalc software—2014).
5
Bimodal Age Distribution Analysis
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Data were analyzed by Microsoft Excel 2007 and SPSS, version 13.0 (SPSS Inc., Chicago, IL, USA). Normality of distribution of continuous variables was tested using Kolmogorov-Smirnov test. Normally distributed variables are presented as frequencies and relative frequencies. Difference in distribution frequency was tested with χ2 test. Continuous variables with non-normal distribution are presented using median and interquartile range (IQR). Histogram was created, and when bimodal distribution was observed, it was divided empirically (23 ), using 50 percentile as a cut-off point. Normality of these two distributions was then tested using Kolmogorov-Smirnov test. Distributions were presented by mode, median, and IQR. P values ≤0.050 were considered statistically significant. Bimodal age distribution was stratified according to variables that were indicative as a cause of bimodality. All questions with binary variables were tested in relation to the first and second modal age distributions by calculating odds ratios (OR) and 95% confidence interval (CI).
6
Statistical Analysis of Experimental Data
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Data are stated as mean ± standard deviation (SD) of three or four independent repeats. The analysis of data along with graphical presentations were performed in Microsoft Excel 2007, SPSS 19 (SPSS Inc., Chicago, IL, USA). According to the Bartlett test, 6/78 samples with p values less than 0.05 were heterogeneous, and 72/78 samples were homogeneous. At the same time, 4/78 samples with p values less than 0.05 did not show normal distribution, and 74/78 samples showed normal distribution (Supplementary Table S1 ).
7
Statistical Analysis of Medical Data
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Tables were prepared with all data obtained from de scans, and such data were
submitted to statistical analysis by a specialized professional of the
InRad/HC-FMUSP.
The data in the present study were analyzed on the basis of a significance level of
5% (α = 0.05), according to the common general standard for the medical and
biological areas. Thus, the calculated values for error probability (p) when ≤
0.05 were considered to be statistically significant, and when > 0.05, were
considered as not statistically significant. The following parameters were
calculated: arithmetic mean, median, minimum and maximum value of the data, standard
deviation, percentiles (0%, 25%, 50%, 75%, 100%), confidence intervals for 95% and
coefficient of variation). Ratios were calculated by means of percentages. The
variations between three or more dependent variables were analyzed by means of the
non-parametric Friedman's statistical test. On their turn, as
post-hoc tests, the variations between pairs of dependent
variables were analyzed by means of the Wilcoxon nonparametric test. Among the most
utilized statistical softwares, the following were utilized for the calculations:
Microsoft Excel 2007; SPSS for Windows (SPSS Inc.) release 10.0.1, 1999; Statistics
for Windows, version 8.0, 2007 (StatSoft Inc.; Tulsa, OK, USA).
submitted to statistical analysis by a specialized professional of the
InRad/HC-FMUSP.
The data in the present study were analyzed on the basis of a significance level of
5% (α = 0.05), according to the common general standard for the medical and
biological areas. Thus, the calculated values for error probability (p) when ≤
0.05 were considered to be statistically significant, and when > 0.05, were
considered as not statistically significant. The following parameters were
calculated: arithmetic mean, median, minimum and maximum value of the data, standard
deviation, percentiles (0%, 25%, 50%, 75%, 100%), confidence intervals for 95% and
coefficient of variation). Ratios were calculated by means of percentages. The
variations between three or more dependent variables were analyzed by means of the
non-parametric Friedman's statistical test. On their turn, as
post-hoc tests, the variations between pairs of dependent
variables were analyzed by means of the Wilcoxon nonparametric test. Among the most
utilized statistical softwares, the following were utilized for the calculations:
Microsoft Excel 2007; SPSS for Windows (SPSS Inc.) release 10.0.1, 1999; Statistics
for Windows, version 8.0, 2007 (StatSoft Inc.; Tulsa, OK, USA).
8
Dyslipidemia Risk Factors Analysis
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The data were analyzed using Microsoft Excel 2007 and SPSS for Windows version 17.0 (SPSS, Inc., Chicago, IL). Continuous variables were reported as mean ± standard deviation (SD) and categorical variables were reported in numbers and/or percentage. The Student’s t test was used to compare two continuous variables, and Chi square test was used to compare categorical variables. One-way analysis of variance (ANOVA) was used to compare three or more subgroups of a continuous variable. Pearson’s bivariate correlation analysis was performed to assess the correlation between dyslipidemia and other independent variables. Multivariate logistic regression was carried out to identify the risk factors that are independently associated with single and mixed dyslipidemia. Statistical significance was set at a p value <0.05.
9
Salinity Regime and Biodiversity Trends
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Statistical treatment was applied in Microsoft Excel 2007 and SPSS 16 (SPSS Inc. Chicago, IL, USA). The regression analysis was used to understand the trends in temporal changes in salinity regime and biodiversity. The standard error is used to understand the reliability of the data. The variance of mean (n = 3) has been evaluated by the Analysis of Variance (one-way ANOVA), followed by Duncan’s post hoc test after testing the dataset for the application of these statistics using the Shapiro–Wilk’s and Levene’s test.
10
Statistical Analysis of Research Results
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Calculations were made with the use of Microsoft Excel 2007 and SPSS 9.0 (SPSS, IL) software. In the statistical analysis, descriptive statistics were used. To analyze the collected research results, a non-parametric test based on Chi-square function was employed. The reliability of the questionnaire was evaluated with Kappa coefficient. The value of alpha <.05 was established as the significance level.
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