Module 5 is based on the preparation and analysis of data. Data which refers to the collection of facts that can be translated into forms that can be processed by computers is an essential aspect of our day to day lives (Silverman, 2018). Thus, the preparation and analysis of data are critical in ensuring the given data meets its need. The data to be analyzed must be of high quality, describable, and presented in a format that can be easily used in the analysis process. Thus, the preparation of data is integral to the success of any successful project in data analytics.
Review of the Website
The provided webpage is part of the Unite for Sight website which focuses on global health by supporting eye clinics worldwide. The organization invests in the human as well as financial resources in their social ventures across the globe to eliminate the existing barriers to eye care. The organization makes use of the existing best practices in the industry for eye care, volunteerism, public health, and social entrepreneurship to achieve its goal of high-quality eye care for every individual across the globe. To effectively utilize these best practices in the industry, there must be a proper means of preparing and analyzing data. For this reason, the given webpage becomes an integral part of the whole website since it deals with the preparation and analysis of data (Unite for Sight, 2019). Also, it is in line with the organization's mission of supporting eye clinics worldwide.
How the Website Can Guide Business Research Proposals and Results
The webpage provided plays an important role in any business research as well as the resulting analysis. The webpage outlines the key areas of data preparation and analysis which include data preparation, descriptive statistics, correlation, inferential statistics, and statistical significance (Unite for Sight, 2019). These subsections of the webpage explain in details what needs to be done with data to achieve the desired results. Thus, the outlined areas are critical in the development of business research proposals as well as in the analysis of data to achieve the desired results.
The webpage focuses on data preparation and analysis. It divides the topic into five subsections which include data preparation, descriptive statistics, correlation, inferential statistics, and statistical significance. Data preparation section outlines the significance of organizing data correctly for analysis (Agresti, 2018). It emphasizes on the accuracy of the data entered through the use of spot-checking or the use of specialized computer programs. The second subsection which is the descriptive statistics focuses on the description of the data without concluding. Descriptive statistics entails the central tendencies, the distribution, the range, and many other aspects of data that would help in drawing meaningful meaning conclusions (Silverman, 2018). The third subsection focuses on the correlation which is one of the most often used kinds of descriptive statistics. Correlation describes the degree of relationship between two variables. The fourth subsection is about the inferential statistics that allows the researchers to make inferences on the data collected based on the hypotheses (Zhang, Zhang, & Yang, 2003). The fifth section focuses on the statistical significance on which the conclusion of any research proposal should be based. The data preparation and analysis section focus on the key areas that must be considered in any given research to achieve the desirable results (Unite for Sight, 2019). However, it does not include the step by step procedure that needs to be followed to achieve the required results.
Agresti, A. (2018). An introduction to categorical data analysis. Wiley.
Silverman, B. W. (2018). Density estimation for statistics and data analysis. Routledge.
Unite for Sight (2019). Data Preparation and Analysis - Research Methodology Course. Uniteforsight.org. Retrieved 23 February 2019, from http://www.uniteforsight.org/research-methodology/module5Zhang, S., Zhang, C., & Yang, Q. (2003). Data preparation for data mining. Applied artificial intelligence, 17(5-6), 375-381.
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