Linear regression and basic statistics (mean, standard deviation) were performed using Excel software (IBM). 95% LLOD was calculated by probit analysis (SPSS, IBM), and the qualitative detection of reference-strain dilutions was compared by Fisher’s exact test (GraphPad QuickCalcs, accessed May 2019).
Excel software
Manufactured by IBM
Sourced in United States
Excel is a spreadsheet software developed by Microsoft. It allows users to organize, analyze, and visualize data through the use of cells, rows, and columns. Excel provides a variety of functions and formulas to perform calculations, create charts, and manage complex data sets. It is a widely used tool for tasks such as budgeting, financial analysis, data reporting, and more.
Automatically generated - may contain errors
Lab products found in correlation
Quickcalc, by GraphPad (1 mentions)
Spss version 20, by IBM (1 mentions)
Origin software version 8, by OriginLab (1 mentions)
Illustrator cs6, by Adobe (1 mentions)
Spss version 19, by IBM (1 mentions)
Spss statistical software package release 22, by IBM (1 mentions)
Statistical package for the social sciences version 20, by IBM (1 mentions)
9 protocols using excel software
1
Statistical Analysis of Experimental Data
2
Evaluating Interrater Agreement: Protocol
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The collected data were organized in a database typed in Excel Software spreadsheet and later exported to IBM SPSS version 20 software for statistical analysis.
Initially, descriptive analyzes of the data were performed and, after that, the calculations of the Kappa coefficient and the Chronbach's Alpha. We used two types of Kappa coefficient: Cohen's Kappa coefficient, which is an association measure used to measure the agreement between the responses of two evaluators; and Fleiss Kappa which has the same purpose, but can be used for more than two evaluators (13) (link)(14) (link) .
Initially, descriptive analyzes of the data were performed and, after that, the calculations of the Kappa coefficient and the Chronbach's Alpha. We used two types of Kappa coefficient: Cohen's Kappa coefficient, which is an association measure used to measure the agreement between the responses of two evaluators; and Fleiss Kappa which has the same purpose, but can be used for more than two evaluators (13) (link)(14) (link) .
3
Gabapentinoids' Therapeutic Efficacy and Safety
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Exposure to gabapentinoids and outcomes (clinical efficacy, adverse effects) were recorded for all those patients who continued the treatment till 8-weeks of the study. Clinical efficacy was defined as a change in mean pain score from baseline to a clinically meaningful reduction of ≥30% or ≥50%.16 (link) Data was converted to electronic files using the Microsoft Excel Software and Excel files were imported to Statistical Package for Social Sciences (SPSS) version 22 for analysis. The quantitative variables like age, dosage of drugs, time to onset of pain relief and difference in pain scores were presented with Mean and Standard Deviations (SD). The qualitative variables such as ethnicity, gender and adverse effects were reported in frequency and percentage. For making comparison of pain scores, time to relief in pain between groups i.e. pregabalin and gabapentin, we used two independent samples Student ‘t’ test. Statistical significance was set at p-value less than 0.05.
4
Soil Element Content Analysis
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Before data analysis, normal distribution was tested using the Kolmogorov–Smirnov (K-S) test using SPSS 20.0 software (SPSS Inc., Chicago, United States). Data were expressed as mean ± standard error. Duncan test was performed to test the significance of differences in bioaccumulation coefficients and translocation factor between groups using SPSS 20.0 software (SPSS Inc., Chicago, United States; p < 0.05). The maximum, minimum, average, standard error, median, and variability of each element content in the soil samples were statistically analyzed using Excel software version 2016 and SPSS software version 23.0. Figures were drawn using Origin software version 8.0 (Origin Lab, Massachusetts, United States), and layout was completed using Adobe Illustrator CS6 (Adobe, United States).
5
Genetic Variants of TREM1 and Glioma Risk
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Independent sample t-test and Pearson’s Chi-squared test were used to assess differences in the information of study subjects (age, gender, etc.). The genotype frequencies distribution of selected SNPs in the control group was evaluated by Pearson’s Chi-squared test to determine whether they were consistent with HWE. The distribution of allele frequencies of selected SNPs between cases and controls were also compared by Pearson’s Chi-squared test. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to evaluate the association between polymorphisms of TREM1 and glioma risk using logistic regression analysis. Codominant, dominant, recessive, and log-additive models were used for testing the relationship between TREM1 SNPs and glioma risk, as shown by PLINK software. In addition, using a bioinformatics database,16 (link),17 (link) we examined the expression level and prognosis of TREM1 in glioma patients. We also performed multivariate analyses of associations among genotypes, various factors, overall survival (OS) and progression-free survival (PFS), with hazard ratios (HRs) and 95% CIs. Multifactor dimensionality reduction (MDR) was conducted to assess SNP–SNP interactions in glioma risk assessment. All statistical analyses were calculated with SPSS software, version 27.0, and Excel software, version 22.0. p <0.05 was considered statistically significant.
6
Statistical Analysis of Quantitative and Qualitative Data
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Excel software and SPSS version 19 were used. Nominal and dichotomic variables were
reported as percentages and quantitative variables having a normal distribution were
reported as mean and standard deviation. Correlation analysis was done with the Spearman
correlation test. A significant statistical difference was considered when a
P > .05 value was reached.
reported as percentages and quantitative variables having a normal distribution were
reported as mean and standard deviation. Correlation analysis was done with the Spearman
correlation test. A significant statistical difference was considered when a
P > .05 value was reached.
7
Toxicity Regression Analysis of Extract
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The growth inhibition rate was converted to an inhibition probability value. The toxicity regression equation, inhibition medium concentration (EC50), and correlation coefficient (R) were calculated using the logarithm of extract concentration and inhibition probability value. Excel software and SPSS statistical software package release 22.0 (SPSS Inc., Chicago, IL, USA) were used to process the relevant data.
8
Non-Normal Data Analysis in Telemetry
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We used Microsoft Excel Software and SPSS software for data collection and analysis.
Normality test (Shapiro–Wilk test) was chosen to assess if the data was normally distributed (H0 = data is normally distributed; H1 = data does not follow a normal distribution).
We found that there were statistical evidences to reject H0 and therefore conclude that the data did not follow a normal distribution. Hence a non-parametric test, Chi-square test (H0 = variable is independent of the type of telemetry; H1 = variable is associated with the type of telemetry) was chosen for further analysis.
Normality test (Shapiro–Wilk test) was chosen to assess if the data was normally distributed (H0 = data is normally distributed; H1 = data does not follow a normal distribution).
We found that there were statistical evidences to reject H0 and therefore conclude that the data did not follow a normal distribution. Hence a non-parametric test, Chi-square test (H0 = variable is independent of the type of telemetry; H1 = variable is associated with the type of telemetry) was chosen for further analysis.
9
Multivariate Statistical Analysis Protocol
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All data were entered and analyzed using Excel Software and the Statistical Package for the Social Sciences version 20 software (SPSS Inc, Chicago, USA). Descriptive statistics (mean, frequencies, percentage, correlations) were performed and used for result interpretation, according to the variables studied. Statistical comparison between groups was done using paired Student's t-test. In order to determine the association among variables, a Pearson's Chi-square non-parametric test was used. Since Chi-square test only determines the existence of association between variables but it does not yield results in the meaning and magnitude of the association, it was decided to measure the degree of association using the Contingency Coefficient (C). Finally, to verify how the variables contribute to the association (correlation), the corrected typified residuals analysis was carried out. Significance was considered with a p < 0.05.
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