# Data Analysis Tools and Application Assignment Sample

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## Introduction of Data Analysis Tools and Application Assignment

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Data Analysis Tools and application is a very important part for analyzing data. In this introduction part we introduce mean-median-mode, correlation, regression and time series as per the data given. We discussed the whole management system of mean median mode and detailed all the groups overall but I focus on the project as well as my methodology. After that I discuss the source of data, where I get the data from. The next step is to ensure how appropriate the data is and I discuss how important the data is for covid 19. I am analyzing and discussing mean-median-mode, correlation, regression and time series from the data. There is a Mean value of Modeled % testing positive for COVID-19, Median value of Modeled % testing positive for COVID-19 and Mode value of Modeled % testing positive for COVID-19. I am also calculating the mean-median-mode value of 95% Lower credible interval. There is reflecting the value of 95% Upper credible interval and also reflecting the all mean-median and mode values. There is also a chat of time series. Time series of Modeled % testing positive for COVID-19. Time series of 95% Lower credible interval. Time series of 95% Upper credible interval. Time series Modeled number of people testing positive for COVID-19. Time series of 95% Lower credible interval and also reflecting the time series of 95% Upper credible interval. And then i prepare a calculation of correlation between Modeled % testing positive for COVID-19 and 95% Lower credible interval, 95% Upper credible interval and also calculate the correlation between Modeled number of people testing positive for COVID-19 and Modeled ratio of people testing positive for COVID-19, 95% Lower credible interval. After that I calculate the regression from the data. I am calculating the regression between Modeled % testing positive for COVID-19 and 95% Lower credible interval, 95% Upper credible interval and I am also calculating the regression between Modeled number of people testing positive for COVID-19 and 95% Lower credible interval, 95% Upper credible interval. This is all the calculations in the project and this is a very knowledgeable project.

#### Analysis

In this part I analyze the mean-median-mode, correlation, regression and time series as per the data given. Now I analyze and discuss each part. The discussion is given below.

### Mean-Median-Mode

This is a mathematical method. Mean, Median and mode helps to identify the average number from the entire list. The mean helps to find the dividing sum and adding numbers by the value of the entire list. This is a most important method to identify the average value from the list. The median value means the middle value. Middle value of the list (Bartha, Á. and Gy?rffy, B., 2019). The middle value between largest and smallest value and the mode value means the most repeated value in the list. These methods are a very important mathematical method to identify the average values from the entire list. There are some formulas of mean-median and mode. The example for the mean value. The mean value of 5,3 and 7 is (5+3+7)/3=15/3=5. 5 is the mean value of 5,3 and 7. This is a very easy process to identify the average value from the number list. The example for median value. The median value of 1,2,3,4,5,6,7,8,9 and 10. The median value is the middle value of the number list. The median value is 5 of the number list, 5 is the middle value. The example of mode value. The mode value of 1,2,3,4,5,6,7,8,9,2,2,10 is 2. The most repeated value is 2. In the number list 2 repeated three times (Belcore, et al, 2021). This is the mode value of the number list.

As the data I am identifying the Mean-Median-Mode value of 95% Upper credible interval, Modeled % testing positive for COVID-19, 95% Upper credible interval, Modeled number of people testing positive for COVID-19, 95% Lower credible interval and 95% Upper credible interval. The Mean value of Modeled % testing positive for COVID-19 is 3.41, the Median value of Modeled % testing positive for COVID-19 6.53 and there is no mode value of Modeled % testing positive for COVID-19. Then the Mean value of 95% Lower credible interval is 3.01. The Median value of 95% Lower credible interval is 5.89 and there is no mode value of 95% Lower credible interval. After that, the Mean value of Modeled number of people testing positive for COVID-19 is 1,03,500. The Median value of Modeled number of people testing positive for COVID-19 is 1,98,350 and the Mode value of Modeled number of people testing positive for COVID-19 is 2,30,400. Then I calculate the Mean value of 95% Upper credible interval is 3.83. The Median value of 95% Upper credible interval is 7.24 and there is no mode value of 95% Upper credible interval (Lechuga, et al, 2020). The Mean value of 95% Lower credible interval is 91,400. The Median value of 95% Lower credible interval is 1,79,200 and the mode value is 2,11,400. Then calculate the Mean value of 95% upper credible interval 1,16,300. The Median value of 95% upper credible interval and in the last part I calculate 2,52,900. The calculation of Mean-Median-Mode, through this method I am able to calculate the average values from the data.

#### Time series

Time series is an observation from the sequence of discrete-time of successive intervals. This is a continuous chart. In this part I am calculating the time series of Modeled % testing positive for COVID-19. Time series of 95% Lower credible interval. Time series of 95% Upper credible interval. Time series Modeled number of people testing positive for COVID-19. Time series of 95% Lower credible interval and also reflecting the time series of 95% Upper credible interval. The all time series are described below (Wang, et al. 2020).

After analyzing the time series of modeled % testing positive for COVID-19 the graph is going high. That means the covid-19 positive cases are growing with the time. Through the time series process I am able to identify the position of modeled % testing positive for COVID-19. From this report I get the highest point of modeled % testing positive for COVID-19 is 8.00 and then the graph falls down to 6.00 and the starting of the test between 2.00 to 4.00. The graph line reflects the all time series scenario of modeled % testing positive for COVID-19.

This graph is reflecting the whole time series frequencies of Time series of 95% Upper credible interval (Muangprathub, et al, 2019). After analyzing this graph the lines of the graph are going high and the line is falling down. That means the Time series of 95% Upper credible interval is growing and then it falling down. Time series of 95% Upper credible interval start line from 3.00 then the highest point is 7.00 that means the 95% Upper credible interval is growing 3.00 to 7.00 and then the line is falling down to 5.00 that means the Time series of 95% Upper credible interval falling down. Through the time series process I am able to find the whole scenario of the Time series of 95% Upper credible interval.

Through this graph I am able to identify the Time series of 95% Upper credible intervals (Zucco, et al, 2020). This graph reflects the 95% Upper credible interval graph line growing high and then the line falling down (Subramanian, et al, 2020). That means the 95% Upper credible interval is growing high from 4.00 to 8.00 and the line is falling down. That means the 95% Upper credible interval is falling down to 7.00. The time series method helps me a lot to identify or make the graph and the method helps me to identify the position of the 95% Upper credible interval.

In this graph I get the position of Time series Modeled number of people testing positive for COVID-19 (Kopaygorodsky, 2019). This graph line is growing high from 1,00,000 to 2,30,00 that means the Modeled number of people testing positive for COVID-19 is growing and then it falling down to 1,70,000 that means the Modeled number of people testing positive for COVID-19 is falling down. The time series method helps me a lot to identify or make the graph and the method helps me to identify Time series Modeled number of people testing positive for COVID-19.

Through this graph I get the actual position of 95% Lower credible interval. This graph line increases from 90,000 to 2,10,000 and then the line falls down to 1,50,000 that means the time series of 95% Lower credible interval falls down. The time series method helps me a lot to identify or make the graph and the method helps me to identify the time series of 95% Lower credible interval (Manalastas, et al, 2021).

Through this graph I am able to identify the time series of 95% Upper credible interval. This graph reflects the whole summary of 95% Upper credible interval. The graph is growing from 1,10,000 to 2,50,000 and then the line is falling down to 2,10,000. That means the Time series of 95% Upper credible interval is increased and then it falls down. The time series method helps me a lot to identify or make the graph and the method helps me to identify the time series of 95% Upper credible interval.

#### Correlation

Correlation is a statistical relationship between two bivariate data. From the data I am correlating between Modeled % testing positive for COVID-19 and 95% Lower credible interval. The correlation value is 0.9956 and I am also correlating between Modeled % testing positive for COVID-19 and 95% Upper credible interval. The correlation value is 0.9953. I am also correlating between the Modeled number of people testing positive for COVID-19 and 95% Lower credible interval. The correlation value is 0.9956 and correlation between the Modeled number of people testing positive for COVID-19 and 95% Upper credible interval. The correlation value is 0.9953. Through the formula of correlation I am able to find the value of correlation (Clements, et al, 2020). Correlation method helps to identify the relation between each other.

Regression

Regression is a statistical method to understand and analyze the reaction of two variables (Kutty, and Abdella, 2020). From the data I am making a Regression between Modeled % testing positive for COVID-19 and 95% Lower credible interval and I am also regretting between Modeled % testing positive for COVID-19 and 95% Upper credible interval. Then I am also making a Regression between the Modeled number of people testing positive for COVID-19 and 95% Lower credible interval. In the next step correlation between the Modeled number of people testing positive for COVID-19 and 95% Upper credible interval (Kang, 2020). I am using the regression formula to solve or identify the relation between two variables.

I am analyzing Mean-median-mode, Time series, Correlation and Regression. This is the all analysis made in this assessment.

Conclusion

In the introduction part I am discussing the all summary and all aims and objectives of the project. In the analysis part I am analyzing Mean-median-mode, Time series, Correlation and Regression. In the Mean-median-mode part, I am reflecting the Mean-median-mode values for each part. In the Time service part I am discussing the all time series graphs and also discussed the ups and downs. Then I reflect the statistical relationship between two bivariate data. In this part also given the values of correlations. In the last part I discussed how to understand and analyze the reaction of two variables. This is all about the knowledgeable project.

Reference list

Journals

Bartha, Á. and Gy?rffy, B., 2019. Comprehensive outline of whole exome sequencing data analysis tools available in clinical oncology. Cancers11(11), p.1725.

Belcore, E., Angeli, S., Colucci, E., Musci, M.A. and Aicardi, I., 2021. Precision agriculture workflow, from data collection to data management using FOSS tools: An application in northern Italy vineyard. ISPRS International Journal of Geo-Information10(4), p.236.

Clements, J., Dolafi, T., Umayam, L., Neubarth, N.L., Berg, S., Scheffer, L.K. and Plaza, S.M., 2020. neuPrint: analysis tools for EM connectomics. bioRxiv.

Kang, H.S., Lee, J.Y. and Lee, D.Y., 2020. An integrated energy data analytics approach for machine tools. IEEE Access8, pp.56124-56140.

Kopaygorodsky, A., 2019, December. Technology of application of software tools for energy technology forecasting. In Proceedings of the 21st International Workshop on Computer Science and Information Technologies (CSIT 2019). Part of series Atlantis Highlights in Computer Sciences (Vol. 3, pp. 267-272).

Kutty, A.A. and Abdella, G.M., 2020. Tools and techniques for food security and sustainability related assessments: a focus on the data and food waste management system. In Proceedings of the 5th NA Conference on Industrial Engineering and Operations Management (pp. 10-14).

Lechuga Sancho, M.P., Martín-Navarro, A. and Ramos-Rodríguez, A.R., 2020. Information Systems Management Tools: An Application of Bibliometrics to CSR in the Tourism Sector. Sustainability12(20), p.8697.

Manalastas-Cantos, K., Konarev, P.V., Hajizadeh, N.R., Kikhney, A.G., Petoukhov, M.V., Molodenskiy, D.S., Panjkovich, A., Mertens, H.D., Gruzinov, A., Borges, C. and Jeffries, C.M., 2021. ATSAS 3.0: expanded functionality and new tools for small-angle scattering data analysis. Journal of Applied Crystallography54(1), pp.343-355.

Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., Wanichsombat, A. and Nillaor, P., 2019. IoT and agriculture data analysis for smart farm. Computers and electronics in agriculture156, pp.467-474.

Subramanian, I., Verma, S., Kumar, S., Jere, A. and Anamika, K., 2020. Multi-omics data integration, interpretation, and its application. Bioinformatics and biology insights14, p.1177932219899051.

Wang, X., Besançon, L., Rousseau, D., Sereno, M., Ammi, M. and Isenberg, T., 2020, April. Towards an understanding of augmented reality extensions for existing 3D data analysis tools. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-13).

Zucco, C., Calabrese, B., Agapito, G., Guzzi, P.H. and Cannataro, M., 2020. Sentiment analysis for mining texts and social networks data: Methods and tools. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10(1), p.e1333.

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