Excel 2003

Manufactured by IBM

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Excel 2003 is a spreadsheet software application developed by Microsoft. It provides users with a tool to organize, analyze, and manipulate data in a tabular format. Excel 2003 allows users to create and format spreadsheets, perform calculations, and generate charts and graphs.

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49 protocols using excel 2003

Groundwater Nitrate Concentration Analysis

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The data was input into Excel 2003 for sorting, and the results were analyzed by SPSS19.0 statistical software. The statistics use descriptive analysis, χ² test. P < 0.05 was considered statistically significant. Principal component analysis (PCA) was used to analyze the relationship between other chemicals in groundwater and nitrate concentration.

Yu G., Wang J., Liu L., Li Y., Zhang Y, & Wang S. (2020). The analysis of groundwater nitrate pollution and health risk assessment in rural areas of Yantai, China. BMC Public Health, 20, 437.

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Analyzing Protozoan Diversity in Beta vulgaris Rhizosphere

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The Microsoft Excel 2003 software, SPSS20.0 statistical software, Origin 8.0 software, PRIMER 7.0 multivariate analysis software and R were used for data processing and correlation analysis of experimental data. R software packages used include maptools, ggplot2, mapproj, pheatmap, and corrplot. The SIMPER subroutine of PRIMER multivariate statistical software was used to calculate and rank the abundance and contribution rate of identified protozoa species in each soil layer. Spearman correlation analysis and biota-environment (BIOENV) analysis were carried out on protozoan abundance detected in Beta vulgaris L. rhizosphere soil and physicochemical properties. The community diversity indices used included the Shannon–Wiener index (H’), Pielou evenness index (J) and Margalef species richness index (d) [27].

Zheng W.B., Wang L., Wang X., Du M.L., Ge C., Wang Q.H., Zhang M.Y., Yang M.L., Zheng X.D., Chen Y, & Lee D.J. (2020). Dominant protozoan species in rhizosphere soil over growth of Beta vulgaris L. in Northeast China. Bioengineered, 11(1), 229-240.

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Comparative ANOVA Analysis of Experimental Data

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All data were run using analysis of variance (ANOVA) with three replicates according to Excel 2003, SPSS 17.0 (SPSS Inc., Chicago, IL, USA). The Duncan’s new Multiple Range (DMR) test at 5% probability level was used to test the differences among the mean values. Significant differences were labelled based on DMR.

Li D., Mo S., Batchelor W.D., Cheng R., Wang H, & Li R. (2021). Effects of nitrogen topdressing and paclobutrazol at different stages on spike differentiation and yield of winter wheat. PeerJ, 9, e12473.

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Statistical Data Analysis Protocol

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Statistical data analysis was performed using Microsoft Excel 2003 and SPSS Statistics 17.0 software. The mean, standard deviation and coefficient of variation were calculated by Microsoft Excel 2003. Drawing was performed using Origin 8.0 software. Image processing was performed using ERDAS IMAUINE 9.2 software, Adobe Photoshop CS6 and Image-Pro plus 6.0 software. The least significant difference (LSD) method was used for multiple comparisons by SPSS Statistics 17.0 software. The LSD test uses the square root of the residual mean square from the one-way analysis of variance (ANOVA) and considers it to be the pooled significant difference.

Li Y., Lv G., Shao H., Dai Q., Du X., Liang D., Kuang S, & Wang D. (2021). Determining the influencing factors of preferential flow in ground fissures for coal mine dump eco-engineering. PeerJ, 9, e10547.

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Two-Way ANOVA for Treatment Effects

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Dependent variables were checked for normality and homoscedasticity and transformed as necessary. Data analysis was performed using Microsoft Excel 2003 (mean values ± standard deviation) and the SPSS 13.0 software package (SPSS Inc., Chicago, IL). The analysis of variance using two-way ANOVA was conducted on the interaction between treatments and sampling time, while one-way ANOVA was applied to evaluate the differences among treatments within one sampling time and within the two sampling times. Separation of means was performed by post hoc test (Tukey’s test), and significant differences were accepted at the levels p ≤ 0.05, 0.01, and 0.001.

Elhawat N., Kovács A.B., Antal G., Kurucz E., Domokos-Szabolcsy É., Fári M.G, & Alshaal T. (2024). Living mulch enhances soil enzyme activities, nitrogen pools and water retention in giant reed (Arundo donax L.) plantations. Scientific Reports, 14, 1704.

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Correlation of BP Changes and Age

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Data were processed in Excel 2003 and analyzed with SPSS10.0. Continuous variables were expressed as mean ±SD. The t-test and the analysis of variance (ANOVA) were used for the statistical analysis. Lineal regress analysis was performed for the correlation between ΔD/D0 and BP parameters, as well as the correlations of ΔD/D0 and ΔBPl-r with age or baseline BP levels. A value of less than 0.05 was considered statistically significant.

Hu W., Li J., Su H., Wang J., Xu J., Liu Y., Huang M, & Cheng X. (2014). The Inter-Arm Diastolic Blood Pressure Difference Induced by One Arm Ischemia: A New Approach to Assess Vascular Endothelia Function. PLoS ONE, 9(1), e84765.

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Genotype-Dependent In-Stent Restenosis

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Data were collected in Microsoft Excel 2003 and were analysed with SPSS 13.0 for Windows (SPSS, Chicago, USA) software. Categorical variables were reported as absolute numbers and percentages, and continuous variables as medians and interquartile ranges. We used the t-test to compare continuous variables between groups, whereas for continuous nonparametric variables we used the Mann–Whitney U-test. Categorical values were compared by using the chi-square test. Multivariate logistic regression has been performed with adjustment for generally known risk factors and MBL variant genotype (A/O + O/O) with in-stent restenosis as a dependent variable, independent variables were entered into the equation simultaneously. The genotype frequency was tested for deviation from Hardy–Weinberg equilibrium by using Pearson’s chi-square test. All analyses were performed two-tailed, and p < 0.05 was considered as significant.

Bagyura Z., Kiss L., Berta B., Szilágyi Á., Hirschberg K., Széplaki G., Lux Á., Szelid Z., Soós P, & Merkely B. (2017). High rate of in-stent restenosis after coronary intervention in carriers of the mutant mannose-binding lectin allele. BMC Cardiovascular Disorders, 17, 4.

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Factors Influencing Non-Invasive Ventilation Failure

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The demographic and clinical quantitative data of the patients were described with the use of centralization and dispersion measures and the qualitative data were described using absolute and relative frequencies. The success or failure of NIV was described according to each characteristic of interest. Chi-square tests were used to assess the association of success or failure for the qualitative characteristics and Mann–Whitney or Student’s t tests for the quantitative characteristics. Simple logistic regression was employed to estimate the odds ratios (OR) of each variable of interest with the occurrence of failure, with 95% confidence intervals (CI).
A multiple logistic regression model was estimated to identify the factors that jointly influence the occurrence of NIV failures, select the variables that in the bivariate tests presented levels of significance below 0.10 (p < 0.10), and use only variables with a significance level lower than 0.05 (p < 0.05) in the final model.
All tests were performed with a significance level of 5%. The software packages used were Excel 2003 and SPSS 20.0. To calculate the sample size, a 5% single-tailed alpha error and a 20% beta error were considered, without taking dropouts into account. The rate of NIV failure considered was 40%. Thus, the calculated sample size was 170 subjects.

S. Rolim D., Galas F.R., Faria L.S., Amorim E.F., Regenga M.M, & Troster E.J. (2020). Use of Noninvasive Ventilation in Respiratory Failure After Extubation During Postoperative Care in Pediatrics. Pediatric Cardiology, 41(4), 729-735.

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Statistical Analysis of Experimental Replicates

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All data were the average of three replicates. The mean of the data and standard deviation (±SD) were calculated using Microsoft Excel 2003, and significant differences were analysed using the SPSS16.0 statistical analysis software.

Ge H.L, & Zhang F.L. (2015). The effects of composite photosynthetic bacterial inoculant PS21 on the biochemical characteristics of wheat seedlings under tetrabromobisphenol A stress. Biotechnology, Biotechnological Equipment, 29(2), 289-298.

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One-Way ANOVA Analysis of Treatments

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All data were analyzed and processed using Excel 2003 and SPSS 10.0 software. One-way ANOVA was employed to calculate the difference in the mean value of each treatment. The mean value is recorded as “mean ± standard error (S.E.).” Duncan's new complex range method was employed to examine the significance of the difference among all treatments (P < 0.05).

Zhou Y., Yang L., Xu S., Li S., Zeng L., Shang H., Li X., Fan H, & Zheng S.J. (2024). Biological control of the native endophytic fungus Pochonia chlamydosporia from the root nodule of Dolichos lablab on Fusarium wilt of banana TR4. Frontiers in Microbiology, 15, 1371336.

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