Statistical Analysis: EU Referendum Data Assignment Sample

Business Analytics

Introduction - Business Analytics

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1: Data Set 1

1.1: Introduction

The major area of emphasis in this report shall be provided on the key areas of statistical calculations concerned with an available data set. To further achieve suitable results and outcomes-oriented with the statistical calculations the additional emphasis on this report shall be further provided on the calculation of descriptive and regression statistical calculations to achieve suitable outcomes relating to the conclusion of the report. The data sets considered for the suitable calculation, analysis, and discussion for this particular report are based on the data sets for the EU referendum considering the socio-economic factors. The additional discussion and emphasis of this report shall be focused on a crucial mention of the predictive tools applicable for the suitable calculation of data sets. The crucial mention of the predictive tools shall further outline the various features, importance, advantages, and disadvantages of the selected predictive tool to further extract suitable evidence in this report. The additional emphasis of this report shall be further focused on a critical reflection for considering the key requirements of a data analytics project. This discussion shall be further revolving around the professional dynamics of an organization, whose primary objective and goal is to fulfil the needs and demands of its respective clients.

1.2: Key Data Features

To achieve suitable statistical exclamations, the primary emphasis of the data set for the EU referendum shall be focused on the calculation of descriptive statistics, which are mentioned as follows.

Descriptive Statistics

Table 1: Descriptive Statistics for Electorate

The above table demonstrates the various calculations of descriptive statistics in which the mean, median, and mode of the observations are calculated as 121983, 96760, and 102209 electorates. 

Table 2: Descriptive Statistics for Votes Cast

The above table demonstrates the descriptive statistics for Votes cast in which the mean votes cast is calculated as 88076, while the median and mode of votes cast are calculated as 72741 and 37975 votes cast.

Table 3: Descriptive Statistics for Valid Votes

The above table demonstrates the descriptive statistics for valid votes in which the mean of valid votes is slated to be 88010 valid votes. Similarly, the median of the valid votes is slated to be 72714 valid votes.

Table 4: Descriptive Statistics for Remain Votes

The above table demonstrates the descriptive statistics for remain votes in which the mean votes are calculated as 42314 votes. The median and mode of remain votes are calculated as 33523 and 36762 votes respectively.

Table 5: Descriptive Statistics for Leave Votes

As per the demonstrations of the above table it can be depicted that the mean of leave votes is calculated as 45695 votes. Subsequently, the median and mode of the leave votes are calculated as 37576 and 35224 respectively.

Table 6: Descriptive Statistics for Median Age

In the context of the above table, the depiction of descriptive statistics for median age is being conveyed in which the mean, median and modal age of the observation have been calculated as 40,41 and 40 respectively. As per the explanations and demonstrations of Amrhein, Trafimow and Greenland (2019), the significance of an average and median age suggests that the majority of the voters in the UK are concentrated on middle-aged persons.

Table 7: Descriptive Statistics for Percent Graduates

The above table of descriptive statistics for percent graduates states that the Kurtosis and Skewness of the observation are calculated as 2.79 and 1.22 respectively. Moreover, the Standard deviation and the sample variance of the observation have been calculated as 7.66 and 58.69 respectively. As opined, narrated and illustrated by Pereira, Pellaux and Verloo (2018), the significance of 7.66 as a standard deviation further represents the data sets close to the mean, in which case the percentage of graduated voters is stated to revolve around the 26-30% mark.

Table 8: Descriptive Statistics for Percent HiProf

As per the demonstrations of the descriptive statistics of Percent HiProf, it can be observed that the range of the data set is slated to be 31.45.

Table 9: Descriptive Statistics for Percent Born Outside UK

As depicted from the above table of descriptive statistics for Percent born outside the UK, the mean and median percentage of people born outside of the UK is considered to be 10 and 7% respectively. As per narrations and opinions of Prabheesh, Padhan and Garg (2020), this further demonstrates a minuscule percentage of people being born outside the UK and representing themselves as voters. 

Table 10: Descriptive Statistics for Percent Born LPR

From the above table of descriptive statistics for lawful permanent residents, the mean and median number of LPR is calculated as 1.18 and 0.84 respectively.

Table 11: Descriptive Statistics for Econ Inactive

From the above table of descriptive statistics for Econ Inactive, it can be depicted that the standard deviation of 3.58 is generally considered to be situated close to the mean. Chaim and Laurini (2018) further expressed and stated that a standard deviation of 3 further represents that the Econ Inactive people are mostly situated in the 30-33 numerical bracket.

Table 12: Descriptive Statistics for Unemployed

The above table further states that the mean number of unemployed population is calculated as 4.

Table 13: Regression Statistics for EU Referendum

From the above table of regression statistics for the EU referendum, it can be stated that the multiple R and R square value of the observation is calculated as 0.9960 and 0.9920 respectively. Moreover, the coefficient and standard error of the observation are slated to be 7492.402332 and 4814508734 respectively. As per narrations and observations of Yabansu et al. (2019), the significance of regression statistics further helps to define the level of dependency between multiple variables of the data set. Therefore, from the above table, it can be concluded that the level of dependency between Total voters to education and median age is considered highly dependent. 

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1.3: Discussion on Predictive Tool

The selection of a predictive tool for suitably calculating the statistical components of an observation and data set is considered an important metric to further acquire requisite values from the data sets. Therefore, the selection of Statistical analysis, machine learning module and search engine optimisation (SEO). As per the statements and illustrations of Be?o (2021), the advantages of statistical analysis further include a data collecting organisation to have a large and vast data set of the EU referendum and extract feasible results in an easy and non-complex manner. Moreover, the implementation of statistical analysis as an important predictive tool can further help the organisation to conduct a detailed market survey of the number of voters and virtually estimate how much growth in the voters and electors list is expected.

The selection of a machine-learning module is further considered a beneficial and significant stage of a predictive tool. Hofmann, Lau and Kirchebner (2022), opined that the advantages of employing a suitable machine learning module further enable an organisation to identify necessary trends and paradigms of the voters based in a constituency. The various trends and paradigms of the voters can be further identified as the orientation of educated voters in an election as well as the concentration of the youth population in the elections. The selection of SEO as a predictive tool also creates a significant amount of favourable circumstances for an organisation. According to Seo (2020), the advantages of SEO mainly consist of the ability for an organisation to filter and select suitable EU referendum data to further understand how much percentage of the voters is unemployed. Necessary steps and actions can be further implemented by the concerned authorities thereafter to minimise the concentration of unemployed individuals in an election.

1.4: Reflection

The significance of a data analytics project is considered an area of utmost importance to get a basic overview of the number of voters present in a constituency as well as interpret the results accordingly through numerical calculations. As per the opinions and explanations of Baijens, Helms and Kusters (2020), the requirements of a data analytics project further include the conduct of three steps which are importing of data, data analysis and communication. In my opinion, the import of data is a critical function of the data analytics project to extract suitable information about the total number of voters in an electorate. I also feel that data analysis using statistical tools is another important metric to fetch suitable data results. I further feel that establishing suitable communication for the conveyance of data analysis is further important for ensuring the true numerical aspects of the imported data. 

2: Data Set 2

2.1: Introduction

In this report, a detailed discussion on the key statistical implications shall be conducted and special adherence shall be further provided to the calculation of statistical figures. Majority of the calculations for the statistical implications shall be further conducted on the available data set which is related to customer purchases and pregnancy. This report shall further revolve around the numerical calculations of descriptive statistics and regression statistics and shall further provide a suitable discussion on the various predictive tools that could be applied by a particular organisation. A self-reflection on the various requirements of a data analytics project shall be further briefly discussed in this report.

2.2: Key Data Features

Descriptive Statistics

Table 14: Descriptive Statistics for Pregnancy Test, Birth Control and Feminine Hygiene

From the above table of descriptive statistics for Pregnancy test, the mean, median and mode of the observation are slated to be 0. This further means that majority of the customers did not buy pregnancy kits recently. From the table of birth control, the mean, median and mode of the observation are also calculated as 0, meaning that the majority of the population did not purchase birth control kits recently (investopedia.com, 2022). The above table of feminine hygiene also presents mean, median and mode, meaning that the majority of the population did not buy feminine products recently.

Table 15: Descriptive Statistics for Folic Acid, Prenatal Vitamins and Prenatal Yoga

From the above table of descriptive statistics for Folic Acid, Prenatal vitamins and Prenatal Yoga the mean, median and mode are calculated as 0. This further signifies that majority share of the customers did not buy these products recently.

Table 16: Descriptive Statistics for Body Pillow, Ginger Ale and Sea Beds

From the above table of descriptive statistics for body pillow, ginger ale and sea beds the mean, median and mode are calculated as 0 respectively. Therefore, this signifies that the majority of the customers did not buy these products.

Table 17: Descriptive Statistics for Stopped Buying Cigarettes, Cigarettes and Smoking Cessation Products

The above table demonstrates that the mean, median and mode for stopped buying cigarettes, cigarettes and smoking cessation products is calculated as 0. This further signifies that the majority of the population in the households are continuing to smoke cigarettes. 

Table 18: Descriptive Statistics for Stopped Buying Wine, Wine, Maternity Clothes and Pregnant

From the above table of descriptive statistics for stopped buying wine, wine and maternity clothes, the mean, median and mode of the observation are found to be zero. However, the mean, median and mode of pregnant population are slated to revolve around 0.5 to 1. This signifies that in every two households, 1 person is found to be pregnant.

Table 19: Regression Statistics for Customer Purchases and Pregnancy

From the above table of regression statistics, the resulting coefficients and standard error are calculated as 0.4136 and 0.1893 respectively. This further signifies that the level of dependency between variables such as being pregnant and changing lifestyle norms as well as buying suitable products is relatively low.

2.3: Discussion on Predictive Tool

Predictive tools are generally used by businessmen to predict their customer's rationale and potential audiences regarding their campaigns and promotions. As per the view of Anshari et al. (2019), predictive Tool regarding the Customer purchases and pregnancy refers to the electronic equipment through which the customer's pregnancy segments are measured. The predictive tools such as medicines, small equipment, reports, and machine models are used to ascertain the no. of customers who are getting pregnant by staying in a household based on recent customer purchases. Predictive tools are “Text Analysis, Real-Time Analysis, Statistical Analysis, Data Mining, and Optimization” (Bates et al. 2018). These tools are used to measure the customer's expectations and it also evaluates the time where the demand of related substances are increased or decreased.

Customer purchases the products such as Feminine Hygiene, Folic Acid, Prenatal Vitamins, Prenatal Yoga, Body Pillow, Ginger Ale, Sea-Bands, Smoking, and Cessation Products, Maternity Clothes, and Pregnancy tool kit. As stated by Chen et al. (2021), the demands and supply of these products are measured by the “Sales Reporting Tools”. Under these, there are various software and retorting tools that help in measuring the quantities that are sold in a fixed period of time. The sales tools are InsightSquared, MixPanel, Intercom, Klipfolio, HubSpot Reporting and DataBox. These tools are the business intelligence tools that help in acquiring the marketing and promotions segment in numerical (Guan et al. 2019). Under this, the account holder's details including the number are put to get the overall access to the products that are sold.

For example- at the time of pregnancy, the demand for intoxicating substances such as wine, and cigarettes are low. As per the author Guha et al. (2021), from the data given in the case study, it can be seen that the businessman has received less income from the four categories. Buying Cigarettes, Products and Stopped Buying Wine. However, the demand for the products such as Feminine Hygiene, Folic Acid, Prenatal Vitamins and Body Pillow rises. This is based on the same time this product is required by the customers (Jai et al. (2021). Therefore it can be seen that the customers are rational in nature. If a product is less in demand, then naturally the other substitute can have more demand than the product.

These reporting tools enable small pieces of information regarding each product with its metrics in a simple way. The CRM and sales tools help to identify the overall required demand and sales of a distinctive product. In a general manner, the Meister Task is the best tool to measure the customer purchaser in the household segment as well as the market segment. The sales tools show the required level of products from the specific category where the people need to attain their satisfaction. As opined by Nusraningrum and Gading (2021), this tool also promotes the reasonable rate segment, s the customers likely to buy good quality products at low prices. The marketing of the above mentioned products is done by the sales analytics where the pregnancy products are in demand when the customers are eager to buy those products in time of need.

These sales promotions are done by getting the information from the legal websites on the customer preferences. The avenues are collected in the form to get all the information that which products are required for the customers who are pregnant. According to the information, the products were made and the life cycle is totally dependent on the customer preferences. As mentioned by Pradeep et al. (2018), generally, customers from household aspects demand these products as they frequently like to conceive. However, in urban areas, the demand for these products creates a competitive opportunity where the customers are rational in nature. This increases the producer's interest to gain more profit by selling the products which are in demand.

The Self-service sites and online communities in the current times show the customer purchases in a brief way. This is because people are mainly spending their time on social sites and media, where they can freely share what they want. From there businessmen can ascertain the demand to a great extent. As per the view of McCarthy et al. (2019), people generally want innovative and cheap rates for the products they want. Therefore from the sales tools and through the social media the requirement and preferences of the customers are easily ascertained. The predictive tools and its software “gather and enrich contact data and customer information”. This is vital for the companies to enrich all the valuable and required data about their customers.

2.4: Reflection

In my opinion, the key requirements for a data analytics project to be effective in meeting the

Needs of the client are the use of predictive tools. In the second data set, the customer's purchases are determined by sales tools that reflect the exact demand of the customers. I think that predictive tools show the effectiveness of the demand where the customer purchases are clearly shown. From there, in my opinion, it can be stated that the demand for these products is low for a certain period. These data are collected to a great extent. As per my knowledge, it can be stated that the clients can give effective reviews if the product and its related data are accurate.

References

Journals

Amrhein, V., Trafimow, D. and Greenland, S., 2019. Inferential statistics as descriptive statistics: There is no replication crisis if we don’t expect replication. The American Statistician, 73(sup1), pp.262-270.

Be?o, M., 2021. The advantages and disadvantages of E-working: An examination using an ALDINE analysis. Emerging Science Journal, 5, pp.11-2

Chaim, P. and Laurini, M.P., 2018. Volatility and return jumps in bitcoin. Economics Letters, 173, pp.158-163.

Hofmann, L.A., Lau, S. and Kirchebner, J., 2022. Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia. Applied Sciences, 12(2), p.819.

Pereira, F., Pellaux, V. and Verloo, H., 2018. Beliefs and implementation of evidence?based practice among community health nurses: A cross?sectional descriptive study. Journal of clinical nursing, 27(9-10), pp.2052-2061.

Prabheesh, K.P., Padhan, R. and Garg, B., 2020. COVID-19 and the oil price–stock market nexus: Evidence from net oil-importing countries. Energy Research Letters, 1(2), p.13745.

Seo, G.H., 2020. Competitive advantages of international airline alliances: a critical review. HOLISTICA–Journal of Business and Public Administration, 11(1), pp.139-145.

Yabansu, Y.C., Iskakov, A., Kapustina, A., Rajagopalan, S. and Kalidindi, S.R., 2019. Application of Gaussian process regression models for capturing the evolution of microstructure statistics in aging of nickel-based superalloys. Acta Materialia, 178, pp.45-58.

Anshari, M., Almunawar, M.N., Lim, S.A. and Al-Mudimigh, A., 2019. Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), pp.94-101.

Bates, D.W., Heitmueller, A., Kakad, M. and Saria, S., 2018. Why policymakers should care about “big data” in healthcare. Health Policy and Technology, 7(2), pp.211-216.

Chen, H., Chan-Olmsted, S., Kim, J. and Sanabria, I.M., 2021. Consumers’ perception on artificial intelligence applications in marketing communication. Qualitative Market Research: An International Journal.

Guha, A., Grewal, D., Kopalle, P.K., Haenlein, M., Schneider, M.J., Jung, H., Moustafa, R., Hegde, D.R. and Hawkins, G., 2021. How artificial intelligence will affect the future of retailing. Journal of Retailing, 97(1), pp.28-41.

Jai, T.M.C., Fang, D., Bao, F.S., James III, R.N., Chen, T. and Cai, W., 2021. Seeing it is like touching it: unraveling the effective product presentations on online apparel purchase decisions and brain activity (An fMRI Study). Journal of Interactive Marketing, 53, pp.66-79.

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Nusraningrum, D. and Gading, D.K., 2021. Purchase Intention of Pregnancy Pillow: Price, Brand Awareness, and Brand Image. PSYCHOLOGY AND EDUCATION, 58(2), pp.4536-4550.

Pradeep, A.K., Appel, A. and Sthanunathan, S., 2018. AI for marketing and product innovation: powerful new tools for predicting trends, connecting with customers, and closing sales. John Wiley & Sons.

Guan, M., Cha, M., Li, Y., Wang, Y. and Yu, J., 2019, February. Predicting time-bounded purchases during a mega shopping festival. In 2019 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 1-8). IEEE.

Baijens, J., Helms, R. and Kusters, R., 2020, May. Data Analytics Project Methodologies: Which One to Choose?. In Proceedings of the 2020 International Conference on Big Data in Management (pp. 41-47).