Data were checked for any errors and inconsistencies and uploaded onto a common server daily. Data were exported to Microsoft Office Excel for cleaning then imported into STATA v15 for analysis. Continuous variables were presented as means with standard deviations. Frequencies and percentages were used for the categorical variables. Categorical characteristics were compared across surveys. Two sample test of proportions was used to compare findings at baseline and endline. A two-sided statistical test was used with 95% confidence interval.
Office excel
Manufactured by StataCorp
Office Excel is a spreadsheet software developed by Microsoft. It is a part of the Microsoft Office suite of productivity applications. Excel's core function is to provide users with a digital platform for organizing, analyzing, and visualizing numerical data through the use of rows, columns, and cells.
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8 protocols using office excel
1
Data Management and Statistical Analysis
2
Factors Affecting Intraocular Pressure
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Data were captured using Microsoft Office Excel and analysed using Stata 14. Continuous numerical data were summarised as mean and standard deviation (SD) and categorical data as percentages (%). The median IOP among eyes that had a higher presenting IOP compared to contralateral eyes was computed. The Mann Whitney test was used to test whether the median IOP differed according to sex, type of primary glaucoma, onset of PCG and laterality of ocular involvement. A P value of <0.05 was considered to be statistically significant.
3
Vitamin D Determinants in Psoriasis
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Data were inserted into a FileMaker Pro database and analyzed with Microsoft Office Excel e STATA MP12 software. Continuous variables were expressed as average ± standard deviation and range, while categorical variables were expressed as proportions. The Ladder of powers test was used to assess the distribution of variables. To assess the determinants of vitamin D values, a multiple linear regression model was designed, using vitamin D levels as outcomes and sex, age, job (indoor/outdoor), menopause status, disease severity and duration, psoriasis clinical type including psoriatic arthritis, current and previous treatment for psoriasis, and season of vitamin D blood testing as determinants. To compare the distribution of the categorical variables in the 3 different groups, the chi-square test was used. To compare quantitative continuous variables, the Kruskal–Wallis test for independent samples with non-normal distribution was used.
A value of P < .05 was considered statistically significant for each test.
A value of P < .05 was considered statistically significant for each test.
4
Predictors of Oxygen Requirement in COVID-19
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All clinical and imaging data items were entered into Microsoft Office Excel and all statistical analyses were performed using STATA statistical software. Categorical variables are presented as frequency and percentages, and continuous variables as the median. ROC curve and Youden's index were used to determine the cut off points for OS1, OS2 and OP. Multinomial logistic regression was used to study the relations of OS1, OS2 and OP with oxygen requirement and place of admission. Hosmer-Lemeshow test was done to test the goodness of fit of our models. A two-sided P value of less than 0.05 was considered to be statistically significant. Details of variables considered are mentioned in Table 2 .
The patient enrolment flowchart As shown inFigure 1 .
The patient enrolment flowchart As shown in
5
Two-Stage DEA for Efficiency Evaluation
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The collected data were checked for completeness, edited, and entered into EpiData, version 3.1. Then exported to Microsoft Office Excel and Stata, version 13, for analysis. Two-stage DEA was performed. At the first-stage DEA, TE scores were identified using DEA Program, version 2.1 (DEAP 2.1), developed by Coelli.26 After the TE scores were determined, the second-stage analysis was identifying predictors for TE. Tobit regression model was employed taking calculated TE score as dependent variable and other environmental and organizational factors as explanatory variables. Significant independent determinants were identified at 95% confidence interval (CI) and P value of less than .05.
6
Antimicrobial Resistance Profiling of Isolates
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The data entry was done using Microsoft Office Excel, and then it was imported to STATA version 14 for further analysis. Descriptive statistics were used to designate the demographic characteristics of the participants and the bacteriological and antimicrobial resistance profiles of the isolates.
7
Statistical Analysis of Numerical and Categorical Data
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Data were captured into Microsoft Office Excel and exported to STATA 2015 for analysis.
Where numerical data were normally distributed, means and standard deviations were used to summarize the data. Where numerical data were not normally distributed, medians and interquartile ranges (IQR) were used to summarize the data. For categorical data, frequencies and percentages were reported.
Where numerical data were normally distributed, means and standard deviations were used to summarize the data. Where numerical data were not normally distributed, medians and interquartile ranges (IQR) were used to summarize the data. For categorical data, frequencies and percentages were reported.
8
Factors Influencing Farm Estrus Synchronization
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Data were coded, entered, and managed by Microsoft Office Excel (2016) and then analyzed by using Stata release 13 statistical software. Descriptive analysis was used to describe the results of the proportion analysis. Chi-square (X2) tests for categorical variables were used to test the relationship between the dependent variable and different independent variables. The association of the different risk factors with farm estrus synchronization practice was calculated. A statistically significant association between variables was considered to exist if the computed p value was less than 0.05.
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