Multicollinearity and Regression Analysis Assignment Sample

Data Management and Analysis - Task 1

Data science is a mainstream subject now-a-days and quality checking is the main criteria to gain trust and confidence from internal and external clients. In case of the data provided in excel spreadsheet, firstly cleaning has been ensured for evaluating validation and checking of quality can help to ensure authentication in data analysis process.

Quantitative data analysis schema can be suggested for given set of data and this can help to provide better insight of study at any situation. Quality checking can be ensured by applying five basic steps given below with justifications.

• Firstly, accuracy of all data is calculated fir understanding whether they are proper or not.
• Secondly, relevancy of data is analysed for acquiring proper information about all requirements.
• Thirdly, completeness of given data is measured to define whether all the components are present or not in study process (Liang et al. 2016).
• Fourthly, timelessness is determined for understanding valuation according to time.
• Finally, consistency of data is also analysed to detect whether the formatting is well enough or not.

Six steps of data cleaning have also ensured for this study to gather information about raw data and it helps to meet goals and expectations of actual practice. Steps of data cleansing process provided in spreadsheet are as follows:

• Primarily, error monitoring has been accomplished to fix incorrect or corrupted data.
• Next, Standardisation of process can be ensured for reducing risk of duplication.
• After that, elimination of duplicate data has been acquired and this helps to provide better understanding about final process (Abedjan, Golab & Naumann, 2016).
• Finally, data analysis is performed by using machine learning tools for better answer.

Data analysis process is also described below and all the steps are evaluated one after another to understand data extraction process.

• Development of research philosophy, design and approach
• Detection of raw data
• Distribution of data according to their size (Ma et al. 2017)
• Sampling
• Development of proper data collection process
• Use of sampling instrument
• Analysis and ethical consideration

Data Management and Analysis - Task 2

London Fire Brigade is one of the prestigious electric supply stations of UK and all the surrounding people are benefitted by this. However, in 2013, government has declared an announcement for cut down of at least ten power stations because of capital loss. Finally, in 2014 closure occurs for these fire stations and the vulnerable impact was greater both for common people as well government. It is reported that job security of 552 firefighters were under stress and 14 engines were not be available. As a result, it becomes quite difficult for British government to provide correct amount of fire engines for any kind of outbreaks. This was actually not accepted by public as fire station is completely government property and it is the duty of government to manage its profit or loss. However, British government is reluctant about this issue and they have always justified about 29-million-euro loss for lockdown of fire stations.

It is vital to acquire proper efficiency and overall performance of London Fire Brigade can be enhanced with the help of the suggestions enlisted below.

• It can be recommended for London Fire Brigade to highlight all the money spent by public and this can help to deliver better authentication in business process (Cooper, 2016).
• It can be suggested for upper management team of London Fire Brigade to implement modern technological equipment by which it becomes possible to gather real time data at any consequences.
• It can be recommended for operation management team of London Fire Brigade to provide quality service and communicate with all the customers in a proper manner.
• It can be suggested for finance team of London Fire Brigade to forecast all the auditing data about revenue and expenditure of income by which it can be possible to understand property consumption (Ronan & Teeuw, 2016).
• It can be recommended for London Fire Brigade to change their working system and proper occupational health and safety is needed to gain more profit in regular business market at any consequences.

Data for London Fire Brigade expenditure was calculated from 2013 to 2019 and the result of structured data shows continuous decrement in expenditure and this is a clear reason for cut down of fire stations. The calculated mean value is 2772.285714 under 95% confidence level with value of standard deviation 61.7352257243634.

Data Management and Analysis - Task 3

Machine Learning technique is a proper methodological technique for research industries to help suitable valuation and reliability for any study. As the speed and complexity of data analysis field is increasing day by day, it is needed to understand machine learning algorithm in a better way. In case of previous dataset, regression analysis could be used for comparing relationship between two or more variables (Austin & Merlo, 2017). This analysis can help to provide critical projection of data and anticipation of result could be better. The above-mentioned machine learning technique can be classified under supervised category and particular numerical value can be explained properly.

Regression analysis is based on prediction of data and structured data are preferable for authentic mathematical calculation. In addition, categorial data can be used for this kind of machine learning process and this helps to retrieve proper answers. Mathematical equation for regression analysis is as follows,

Y=mx + b

Here, in the above equation, formula of straight line is followed that helps to provide better understanding about data and approximation in observation can also be acquired in a better manner. On the other hand, this is a good choice of machine learning for data analysis purpose and this can a help to detect the value of multiple variables at a time. Multi-dimensional space can help to provide accurate result and calculation can be authentic in nature (Daoud, 2017). In addition, detection of slope and error can also be possible by using regression analysis and this provides better information about comparison between two variables.

This supervised machine learning technique could help to provide better insight about retrieved data for closing of London Fire Brigade in 2014. On the other hand, quality analysis of data can also be ensured in a perfect manner with the help of this method and both the reliability and validity for analysis process can also be acquired in a better way (Valaskova, Kliestik & Kovacova, 2018).

References for Multicollinearity and Regression Analysis

Abedjan, Z., Golab, L., & Naumann, F. (2016, May). Data profiling. In 2016 IEEE 32nd International Conference on Data Engineering (ICDE) (pp. 1432-1435). IEEE. Retrieved from: https://www.researchgate.net/profile/Felix_Naumann/publication/317588042_SIGMOD_2017_Tutorial_Data_Profiling/data/59417f11458515a36b57275a/SIGMOD-2017-Tutorial-Data-Profiling.pdf

Austin, P. C., & Merlo, J. (2017). Intermediate and advanced topics in multilevel logistic regression analysis. Statistics in medicine, 36(20), 3257-3277. Retrieved from: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.7336

Cooper, A. (2016). Maximising Performance While Reducing Resources at London Fire Brigade. Impact, 2(1), 38-41. Retrieved from: https://orsociety.tandfonline.com/doi/pdf/10.1080/2058802X.2016.11963996

Daoud, J. I. (2017, December). Multicollinearity and regression analysis. In Journal of Physics: Conference Series (Vol. 949, No. 1, p. 012009). IOP Publishing. Retrieved from: https://iopscience.iop.org/article/10.1088/1742-6596/949/1/012009/pdf

Liang, X., Li, S., Zhang, S., Huang, H., & Chen, S. X. (2016). PM2. 5 data reliability, consistency, and air quality assessment in five Chinese cities. Journal of Geophysical Research: Atmospheres, 121(17), 10-220. Retrieved from: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016JD024877

Ma, X., Zhang, J., Zhang, Y., & Ma, Z. (2017). Data scheme-based wireless channel modeling method: motivation, principle and performance. Journal of Communications and Information Networks, 2(3), 41-51. Retrieved from: https://link.springer.com/content/pdf/10.1007/s41650-017-0009-7.pdf

Ronan, T., & Teeuw, R. (2016). London’s burning: integrating water flow rates and building types into fire risk maps. International Journal of Emergency Services. Retrieved from: https://researchportal.port.ac.uk/portal/files/3641302/Londons_burning.pdf

Valaskova, K., Kliestik, T., & Kovacova, M. (2018). Management of financial risks in Slovak enterprises using regression analysis. Oeconomia Copernicana, 9(1), 105-121. Retrieved from: http://economic-research.pl/Journals/index.php/oc/article/download/718/683

Website

Statista.com, (2020). Public sector expenditure on fire-protection services in the United Kingdom (UK) from 2011/12 to 2018/19. Retrieved from: https://www.statista.com/statistics/298661/united-kingdom-uk-public-sector-expenditure-fire-protection-services/. [Retrieved on: 15.09.2020]

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