The data were entered and cleaned using Microsoft excel 2019 and analyzed using Statistical Analysis System (SAS®), version 9.2 for Mac. Continuous variables were summarized and reported as means and standard deviations (SDs), then compared across study groups using the T-test. Categorical variables were expressed as percentages and compared across the 2 study groups using the Chi-square test. A p-value of <0.05 was considered significant. The significant variables were further included in the multivariable regression model to identify the unique contribution of each variable.
Excel 2019
Manufactured by SAS Institute
Excel 2019 is a spreadsheet software application developed by Microsoft. It is primarily used for organizing, analyzing, and visualizing data. Excel 2019 includes features for creating tables, charts, and formulas to perform various calculations and data manipulations. The core function of Excel 2019 is to provide users with a tool for managing and working with numerical and textual data.
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Lab products found in correlation
7 protocols using excel 2019
1
Statistical Analysis of Continuous and Categorical Variables
2
Circadian Rhythm and Gut Microbiome Analysis
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Data analysis was carried out in Excel 2019, SAS 9.4, QIIME, GraphPad Prism software (V.8.0.2), R program (V.3.6.1), STAMP (V.2.1.3), and the Galaxy cloud platform. Hormone concentrations, relative abundance of rhythmical bacterial taxa and ruminal fermentation parameters between light/dark conditions in vivo, and the relative abundance of bacteria taxa in vitro were analyzed using paired t-tests, with P < 0.05 considered as significant. The predicted pathways were compared using the Welch's t-test performed by STAMP with a P < 0.05 being considered significant. Correlation analysis was performed using Spearman's method in R software.
3
Statistical Analysis of Experimental Data
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The experiment was repeated three times, the results were expressed as average ± standard deviation of three replicates and means represented by different letters in each column show significant difference at 0.05 level according to Duncan’s t test. Excel 2019 and SAS 8.01 were used to sort out and analyze the data obtained in the experiment. We performed a visual analysis of the data using orthogonal design assistant II (v 3.1) software.
4
Fatty Acid Composition and Gene Expression
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The content and composition of fatty acid as well as the RT-qPCR data were arranged using Excel 2019 software, and were then analyzed using SAS 8.0 software (SAS Institute Inc., Cary, NC). The relative mRNA expression of target genes was calculated using the comparative Ct method (2−ΔΔCt methods) (Livak and Schmittgen, 2001 (link)). The means of different groups were subjected to ANOVA testing, and the means were assessed for significance using the Duncan's Multiple Range test. Results were presented as the mean ± S.E.M. Differences were considered statistically significant at P < 0.05. In addition, principal component analysis (PCA ) was carried out using SPSS 26.0 (IBM, Armonk, NY) software to identify the main factors that contributed to fatty acid composition. The PC values were calculated by using equations: j = 1, 2, 3, … , k; Fj, the scores of PC; aj1 ∼ ajp, the factor score coefficients of PC on variables; X1 ∼ Xp, the fatty acids profiles of each sample; j represented the number of PC; p represented the number of variables. Subsequently, the Pearson’ s correlation was calculated between principal component value and fatty acid composition as well as genes expression.
5
Comparative Analysis of NGI-1 Virus Treatments
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Analysis of variance (ANOVA) followed by Tukey’s multiple-comparison test was used to estimate and compare the viral titers in NGI-1-treated versus untreated tissue culture supernatant samples. NA activities and NLG site occupancy of and frequency of morphologically normal and abnormal NGI-1-treated versus untreated viruses were compared using Student’s unpaired t test. A linear mixed model with repeated measures was used to compare the immunogenicity of NGI-1-treated versus untreated HA. A P value of <0.05 was considered significant for all comparisons. Statistical analyses were performed using GraphPad Prism 8.0, Excel 2019, and SAS 9.4.
6
Tooth Pulp Volume Correlates with Age
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All statistical values were analysed using Microsoft Excel 2019 and the SAS V8 and the significance level for this study was set to 5%. The following morphological variables were recorded for each sample's maxillary left central incisor and canine: pv = the volume of pulp cavity, tv = the volume of the entire tooth, and PTR = pulp/tooth volume ratio. The Pearson correlation coefficient between the PTR value and the chronological age was calculated.
7
Statistical Analysis of Experimental Data
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All experimental data were analyzed using Microsoft Excel 2019 and SAS 8.0. Statistical significance was set at p < 0.05.
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