Data Analysis And Data Evaluation Assignment Sample

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Introduction

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The process of data evaluation and analysis is majorly used for monitoring, investigating, transforming, and marketing for the purpose of collecting informational data and also for helping in the discussion making of a company. For instance, informational data plays a significant role in the critical decisions of companies.

In respect to the given assignment, the data of almost 75 districts of Nepal needed to be evaluated for understanding the correlation between different variables and values of the variables of all the 75 districts of Nepal. Several curriculums such as the histogram chart, descriptive statistics and correlation analysis where required to do in the report for providing the relationship between to selected variables from the data set. The implementation of the outcome is required for practical purposes and also a summarization of the report is needed.

Data Analysis

The data is of the 75 different districts of Nepal which is evaluated through various numbers of measures. The total number of measures was collectively known as the descriptive analysis in which the data is analyzed through different mediums such as mean, median, mode, range, standard deviation and many more. In many aspects, the latest versions of Excel for Windows and Macintosh are identical. The visual screens may alter slightly, however, this is due to the operating system as much as it is to the excel application. (Neil J. Salkind, 2016). Similarly, the value of median provides the middle number in the total data set of a single variable. On the other hand, the value of range provides information about the value variation of each variable in the data set.

Selection of Variables for Exploring Correlation

According to the entire data set of the 75 different districts of Nepal, the variable of population and the poverty index is the most eligible to select for the correlation analysis. The reason behind the selection of population and poverty index is to provide information about the total number of people who are present in sections below the poverty line in the different districts of Nepal. It is also valuable information as it can be used by the government to evaluate the total numbers of people present below the poverty line of the country. Such trends affect geography as well. According to a recent International Benchmarking Review, the United Kingdom ranks top in the world for human geography. However, the same study finds a lack of quantitative training and knowledge, as well as a "surprising underinvestment" in Geographical Information Systems (GIS) (Richard Harris and Claire Jarvis, 2015). The outcome of the correlation between the population and poverty index will provide information on the dependence of the poverty index on the total population of the country.

Analysis of Correlating Variables

The variable of the population indicates the total number of people present in each district of Nepal. The total sum of the population of all the districts of Nepal will provide information on the total population present in the entire country. The advancement of geographic information systems, the growing availability of spatial data, and recent advances in methodological techniques have all combined to make this an exciting time to research geographic issues. (Peter A. Rogerson, 2019). Therefore, the information of the population is important and can be effectively utilized during the correlation analysis.

The variable of the total poverty index indicates the total number of people who are present below the poverty line in each district of Nepal. The total sum of the people present below the poverty line will provide information on the total number of people present below the poverty line in the entire country if all the data of different districts are added. Traditionally, librarians have made judgments based on their instincts and expertise. A library, on the other hand, can adopt a "culture of evaluation," in which judgments are made based on facts, study, and analysis (Derek Rowntree,2000). This information is important for the discussion of improvement and changes in the country by the government.

Analysis of other variables

The other variable which is present in a datasheet of Nepal is the total percentage of children who are malnourished and below the age of 5. This data is evaluated in the second sheet of the given MS Excel sheet and also the total number of children was calculated from the percentage of it. Green technology innovation is the key driving force behind China's strategic emerging industries' long-term development. Improving the efficiency of green technology innovation is a good method to meet this goal (Luo et al .2019). All the other variables were calculated in the second sheet of MS Excel. All the data are calculated from the total population in each district and the percentage of variables was calculated in total numbers.

The hypothesis of the Correlating Variables Relations

The practical hypothesis of the correlating variables is the total value of population and the total value of people present below the poverty line in the different districts of Nepal. According to the analysis of correlation, data would be helpful to the government to run a program for the growth and development of the country. Sustainable development difficulties are increasingly concentrated in cities as the world's population migrates to cities, putting immense pressure on society to create more sustainable, innovative, and egalitarian urban settings (Neves et al .2020). It also provides information about the total goat levels and development prospects of the country on the global platform. After calculating both information of population and people present below the poverty line is around a negative of 0.21. This calculation shows that the total population of the country and the total number of people present below the poverty line are marginally related to each other but not dependent on other variables. It can be considered that if the total population increases in the country then the total number of people below the poverty line also increases but with slight margin values.

Strength of the Correlating Variables Relations

The significant strength of the relation between both the correlated variables is to understand the growth rate and development rate of the country in all the 75 different districts of the country. It will also help to understand the total per capita income of the general public of the country and help in decision-making for the development and progress programs that will be run in the country for growth prospects. In accordance with the descriptive analysis, the value of mean represents the average total value of each variable present in the data set of Nepal. Similarly, the value of median provides the middle number in the total data set of a single variable. The goal of this essay is to give an overview of an organized, rigorous approach to collaborative qualitative analysis while also addressing the limitations of working in a team setting. The method is based on the constant comparative method and qualitative data analysis literature linked to thematic analysis (Richards et al .2021). On the other hand, the value of range provides information about the value variation of each variable in the data set.

Nature of data variation for each Variable

The nature of variation in each variable is also considered as the total range value of each variable. The total value of the range is also calculated on the first sheet of Excel and provides informational data about the value variation of each variable. Real Driving Emissions (RDE) test procedures, which have been in effect for new Euro 6 cars from September 2017, were developed in response to the requirement to verify vehicle emissions in real-world conditions (Varella et al .2019). The range for the people who are present below the poverty line is 32.7 6 and for the children who are malnourished and below the age of 5 is 49.5. Similarly, the range of population is around 682304 and the range of adult literacy is around 50.15.

Relationship of data through Scatter Plot chart

The relationship between the two variables of population and the total number of people present below the poverty line in the country is represented through a scatter plot chart. According to the chart, it is clear that both the variables are correlated with each other because as the rate of population increases the total number of people present below the poverty line also increases and vice versa. Therefore, the correlation between both variables is accurate according to the projected chat.

Hypothesis test

The hypothesis test can be done according to the represented correlation between the two variables which is the population and the people present below the poverty line in the country. Respiratory movements can considerably harm the quality of PET images. The motion blurs the radioactive distribution, resulting in a picture that may be poor for activities such as diagnosis and quantitative assessment (Walker et al .2018). The test can be done for the purpose of utilizer the growth and development prospect but with the help of the Government and the National association of population and poverty control departmenThe implicationtion of Relationship Outcome

The realistic implication of the relationship outcome between the two variables, which is the population and the total people present below the To order to help in the development and growth program of the country for the future aspect and also for development prospects. It is also helpful to the government for understanding the per capita income rate and the distribution of per capita income among the people of Nepal in depth. It is vital to have the infrastructure of an existing software package to support the development of new algorithms. Because there was no suitable open-source package, the DIALS project was started to provide this platform (Winter et al .2018). Implication of the outcome can also be used for understanding the population and growth rate at the same time.

Conclusion

Here it is concluded from the above section of the assignment that the data is of the 75 different districts of Nepal which is evaluated through various numbers of measures. In accByescriptive analysis, the value of mean represents the average total value of each variable present in the data set of Nepal. Similarly, the value of median provides the middle number in the total data set of a single variable. On the other hand, the value of range provides information about the value variation of each variable in the data set. The reason behind the selection of population and poverty index is to provide information about the total number of people who are present in sections below the poverty line in the different districts of Nepal. The relation between the population and the total people present below the poverty line is helpful to the government for understanding the per capita income rate and the distribution of per capita income among the people of Nepal in depth.

Reference list

Journals

Neil J. Salkind (2013 or 2016) Statistics for People Who (thinktheatrettee Statistics. SAGE Publications.

Richard Harris and Claire Jarvis (2011 or 2015) Statistics for Geography and Environmental Science. Prentice Hall.

Peter A. Rogerson (2019), Statistical Methods for Geography: A Student's Guide (5th Edition). SAGE Publications.

Derek Rowntree (2000) Statistics Without Tears: An Introduction for non-mathematicians. Penguin Books

Koning, A.J., Rochman, D., Sublet, J.C., Dzysiuk, N., Fleming, M. and Van der Marck, S., 2019. TENDL: complete nuclear data library for innovative nuclear science and technology. Nuclear Data Sheets, 155, pp.1-55.

Luo, Q., Miao, C., Sun, L., Meng, X. and Duan, M., 2019. Efficiency evaluation of green technology innovation of China's strategic emerging industries: An empirical analysis based on Malmquist-data envelopment analysis index. Journal of Cleaner Production, 238, p.117782.

Neves, F.T., de Castro Neto, M. and Aparicio, M., 2020. The impacts of open data initiatives on smart cities: A framework for evaluation and monitoring. Cities, 106, p.102860.

Richards, K.A.R. and Hemphill, M.A., 2018. A practical guide to collaborative qualitative data analysis. Journal of Teaching in Physical Education, 37(2), pp.225-231.

Varella, R.A., Faria, M.V., Mendoza-Villafuerte, P., Baptista, P.C., Sousa, L. and Duarte, G.O., 2019. Assessing the influence of boundary conditions, driving behavior,r and, data analysis methods on real driving CO2 and NOx emissions. Science of the total environment, 658, pp.879-894.

Walker, M.D., Bradley, K.M. and McGowan, D.R., 2018. Evaluation of principal component analysis-based data-driven respiratory gating for positron emission tomography. The British journal of radiology, 91(1085), p.20170793.

Winter, G., Waterman, D.G., Parkhurst, J.M., Brewster, A.S., Gildea, R.J., Gerstel, M., Fuentes-Montero, L., Vollmar, M., Michels-Clark, T., Young, I.D. and Sauter, N.K., 2018. DIALS: implementation and evaluation of a new integration package. Acta Crystallographica Section D, 74(2), pp.85-97.

Zhang, Z., Li, H., Jiang, S., Li, R., Li, W., Chen, H., and Bo, X., 2019. A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data. Briefings in bioinformatics, 20(4), pp.1524-1541.

Zhu, Q., Wu, J. and Song, M., 2018. Efficiency evaluation based on data envelopment analysis in the big data context. Computers & Operations Research, 98, pp.291-300.