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Data Analysis and Data Evaluation Assignment Sample

Introduction - Data Analysis and Data Evaluation

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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 critical decisions of the 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 accordance with the descriptive analysis the value of mean represents the average total value of each variable present in the data set of Nepal (Brown et al 2018). 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 the 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 (Delgado et al .2017). The outcome of the correlation between the population and poverty index will provide the information of the dependence of the poverty index on the total population of the country.

Analysis of Correlating Variables

Histogram Chart of Population variable by new assignment

Figure 1: Histogram Chart of Population variable.

(Sources: Self Created in MS Excel)

The variable of 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 the information of total population present in the entire country (Inkster et al .2018). Therefore, the information of population is important and can be effectively utilized during the correlation analysis.

Histogram Chart of Poverty Index variable by new assignment

Figure 2: Histogram Chart of Poverty Index variable.

(Sources: Self Created in MS Excel)

The variable of total poverty index indicates the total number of people who are present below the poverty line in each district of Nepal. Total sum of the people present below the poverty line will provide information of the total number of people present below the poverty line in the entire country if all the data of different districts are added (Koning et al .2019). 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 data sheet of Nepal is the total percentage of children which 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 (Luo et al .2019). All the other variables were calculated in the second sheet of the MS Excel. All the data are calculated from the total population in each district and the percentage of variables were calculated in total numbers.

image of Scattered plot chart of all present variables

Figure 3: Scattered plot chart of all present variables.

(Sources: Self Created in MS Excel)

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 for the purpose of running a program for the growth and development of the country (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 totally 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 which 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 (Richards et al .2021). On the other hand, the value of range provides the 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 range is also calculated on the first sheet of Excel and provides informational data about the value variation of each variable (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

image of Scattered plot chart of population and people present below the poverty line variables

Figure 4: Scattered plot chart of population and people present below the poverty line variables.

(Sources: Self Created in MS Excel)

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 totally 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 the 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 (Walker et al .2018). The test can be done for the purpose of utilizing the data for the growth and development prospect but with the help of the Government and National association of population and poverty control department.

Implication 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 poverty In 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 (Winter et al .2018). Implication of the outcome can also be used for understanding the population and growth rate at the same exact 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 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. On the other hand, the value of range provides the 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

Brown, N.C., Altadmri, A., Sentance, S. and Kölling, M., 2018, August. Blackbox, five years on: An evaluation of a large-scale programming data collection project. In Proceedings of the 2018 ACM Conference on International Computing Education Research (pp. 196-204).

Delgado, H., Todisco, M., Sahidullah, M., Evans, N., Kinnunen, T., Lee, K.A. and Yamagishi, J., 2018, June. ASVspoof 2017 Version 2.0: meta-data analysis and baseline enhancements. In Odyssey 2018-The Speaker and Language Recognition Workshop.

Inkster, B., Sarda, S. and Subramanian, V., 2018. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR mHealth and uHealth6(11), p.e12106.

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 Sheets155, pp.1-55.

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 Sheets155, 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 Production238, 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. Cities106, p.102860.

Richards, K.A.R. and Hemphill, M.A., 2018. A practical guide to collaborative qualitative data analysis. Journal of Teaching in Physical Education37(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 and data analysis methods on real driving CO2 and NOx emissions. Science of the total environment658, 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 radiology91(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 D74(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 bioinformatics20(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 Research98, pp.291-300.

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