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Spss statistical for windows

Manufactured by IBM
Sourced in United States

SPSS Statistical for Windows is a software application designed for statistical analysis. It provides users with tools for data management, analysis, and presentation. The software is capable of performing a wide range of statistical procedures, including descriptive statistics, regression analysis, and hypothesis testing. SPSS Statistical for Windows is commonly used in the fields of social sciences, market research, and healthcare.

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Lab products found in correlation

4 protocols using spss statistical for windows

1

Biofilm Formation and Growth Kinetics

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All tests were performed in triplicate of independent experiments and analyzed on IBM SPSS Statistical for Windows (version 20.0, IBM Corp., Armonk, NY, USA, 2011). The growth kinetics data were checked for normality using the Shapiro-Wilk’s test and analyzed by one-way analysis of variance (ANOVA) with Tukey’s post-test (p ≤ 0.05). The biofilm data were analyzed using Mann-Whitney and Kruskal-Wallis tests for comparisons between the groups and CAT concentrations (p ≤ 0.05).
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2

Questionnaire Analysis Protocols and Practices

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To avoid only part analysis of any questionnaire, any non-completed full sections of the questionnaire led to that entire questionnaire being completely withdrawn from the analysis. This removed 6 participants from the final analysis, to leave 271 participants in total. All responses were anonymized via an identification number and their subsequent results inputted into IBM SPSS Statistical for Windows (version 24.0, IBM Corp.: Armonk, NY, USA). Chi square statistical analysis were undertaken on all quantitative data and coding analysis completed on all qualitative responses. One multiple choice question focused on demographic information in order for direct comparison to be made across sex.
Ethical approval was granted from the ethic committees of both Canterbury Christ Church University (UK) and Cork Institute of Technology (Ireland) in March 2018.
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3

Anopheles Mosquito Larval Ecology

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Larval breeding habitats and number of immature Anopheles mosquitoes sampled were described using tables. Correlation analysis was used to investigate the relationship between pH, temperature and water depth to the Anopheles larval density. Anopheles larval density was determined as the number of Anopheles larvae (early or late) divided by the number of dips taken from each larval habitat. Larval density was log transformed log10 (x + 1) to improve the normality of distribution. Multiple regression analysis was used to identify the environmental variables associated with the occurrence of Anopheles larvae. Mann–Whitney U test was used to compare samples with two variables; presence of algae (presence or absence), habitat permanency (temporary or permanent), surface debris (present or absent), intensity of light (sunlit or shaded) and water movement (still or flowing). Kruskal–Wallis H test was used to compare samples with more than two groups: water turbidity, water perimeter, distance to the nearest house, canopy cover, emergent plant coverage, habitat type and substrate type. These non-parametric tests were used to compare larval densities from sites with different habitat characteristics.
Data were analysed using IBM SPSS statistical for Windows (IBM corp., Armonk, NY), version 20.0. Values were considered significantly different if p < 0.05 for all the tests.
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4

Haematological Analysis in Blood Disorders

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A descriptive analysis was performed to define the characteristics of the study sample through a form of counts and percentages. The normality of the data was checked using a histogram, Kolmogorov–Smirnov Lillefors and Levene's tests. A chi‐square test was used to establish a relationship between categorical variables. Accordingly, haematological parameters were compared between males and females using a parametric test (i.e. independent t tests), while the non‐parametric Kruskal–Wallis test was used to compare haematological parameters and medical conditions (i.e. cancer, hereditary blood diseases and immune deficiency syndromes). Binary logistic regression analysis was applied to identify factors associated with the occurrence of OM at univariate and multivariable levels. The statistical significance was assumed at a 5% level, and the statistical analysis was performed using the Statistical Package for Social Science (Released 2015, IBM SPSS Statistical for Windows, Version 23.0, Armonk, NY: IBM Corp).
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