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

Electorate
Mean	121983.9423
Standard Error	4972.532913
Median	96760
Mode	102209
Standard Deviation	97059.97033
Sample Variance	9420637840
Kurtosis	53.76588074
Skewness	5.601606435
Range	1259156
Minimum	1799
Maximum	1260955
Sum	46475882
Count	381
Confidence Level (95.0%)	9777.125104

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. 

Votes Cast
Mean	88076.56168
Standard Error	3257.919246
Median	72741
Mode	37975
Standard Deviation	63592.04673
Sample Variance	4043948407
Kurtosis	42.88572813
Skewness	4.928880305
Range	789099
Minimum	1424
Maximum	790523
Sum	33557170
Count	381
Confidence Level(95.0%)	6405.80658

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.

Valid Votes
Mean	88010.07349
Standard Error	3255.413965
Median	72714
Mode	#N/A
Standard Deviation	63543.14559
Sample Variance	4037731351
Kurtosis	42.92962405
Skewness	4.931265277
Range	788725
Minimum	1424
Maximum	790149
Sum	33531838
Count	381
Confidence Level(95.0%)	6400.880629

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.

Remain Votes
Mean	42314.74803
Standard Error	1826.405129
Median	33523
Mode	36762
Standard Deviation	35650.00589
Sample Variance	1270922920
Kurtosis	43.71551274
Skewness	4.966922156
Range	439904
Minimum	803
Maximum	440707
Sum	16121919
Count	381
Confidence Level (95.0%)	3591.125841

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.

Leave Votes
Mean	45695.32546
Standard Error	1601.78824
Median	37576
Mode	35224
Standard Deviation	31265.65913
Sample Variance	977541440.6
Kurtosis	27.98726763
Skewness	3.993978846
Range	348821
Minimum	621
Maximum	349442
Sum	17409919
Count	381
Confidence Level (95.0%)	3149.478201

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.

Median Age
Mean	40.5984252
Standard Error	0.219059372
Median	41
Mode	40
Standard Deviation	4.27586835
Sample Variance	18.28305015
Kurtosis	-0.002903845
Skewness	-0.45956062
Range	22
Minimum	29
Maximum	51
Sum	15468
Count	381
Confidence Level (95.0%)	0.430720303

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.

Percent Graduates
Mean	26.91406418
Standard Error	0.392496166
Median	25.70996337
Mode	#N/A
Standard Deviation	7.661219528
Sample Variance	58.69428466
Kurtosis	2.796299256
Skewness	1.224033124
Range	54.14939814
Minimum	14.21477655
Maximum	68.36417469
Sum	10254.25845
Count	381
Confidence Level (95.0%)	0.771736294

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.

Percent HiProf
Mean	10.25338244
Standard Error	0.195791057
Median	9.50274242
Mode	#N/A
Standard Deviation	3.82168896
Sample Variance	14.6053065
Kurtosis	4.700398516
Skewness	1.439861394
Range	31.45378056
Minimum	4.014746473
Maximum	35.46852703
Sum	3906.53871
Count	381
Confidence Level (95.0%)	0.384969529

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.

Percent Born Outside UK
Mean	10.63536959
Standard Error	0.513104376
Median	7.174176927
Mode	#N/A
Standard Deviation	10.01539786
Sample Variance	100.3081944
Kurtosis	5.27743223
Skewness	2.31189262
Range	52.93171093
Minimum	2.151430945
Maximum	55.08314188
Sum	4052.075816
Count	381
Confidence Level(95.0%)	1.008879331

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. 

Percent Born LPR
Mean	1.175614156
Standard Error	0.0580326
Median	0.837097887
Mode	#N/A
Standard Deviation	1.13275116
Sample Variance	1.28312519
Kurtosis	8.94054331
Skewness	2.665851252
Range	7.501718746
Minimum	0.119303269
Maximum	7.621022015
Sum	447.9089934
Count	381
Confidence Level (95.0%)	0.114105226

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.

Econ Inactive
Mean	29.8861665
Standard Error	0.183555079
Median	29.76578726
Mode	#N/A
Standard Deviation	3.582852198
Sample Variance	12.83682987
Kurtosis	-0.227710034
Skewness	0.03723038
Range	21.29943917
Minimum	17.94081381
Maximum	39.24025298
Sum	11386.62944
Count	381
Confidence Level (95.0%)	0.360910827

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.

Unemployed
Mean	4.050482445
Standard Error	0.063254412
Median	3.897011066
Mode	#N/A
Standard Deviation	1.234676866
Sample Variance	1.524426963
Kurtosis	0.077908151
Skewness	0.672481229
Range	6.915021544
Minimum	1.10974106
Maximum	8.024762604
Sum	1543.233811
Count	381
Confidence Level (95.0%)	0.12437249

Table 12: Descriptive Statistics for Unemployed

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

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.996029748
R Square	0.992075258
Adjusted R Square	0.991839019
Standard Error	8768.217981
Observations	381
ANOVA
df	SS	MS	F	Significance F
Regression	11	3.55147E+12	3.22861E+11	4199.457229	0
Residual	369	28369327584	76881646.57
Total	380	3.57984E+12
Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	7492.402332	4814508734	1.55621E-06	0.99999875	-9467308015	9467323000	-9.467E+09	9467323000
X Variable 1	-32.84913075	18.19046618	-1.80584326	0.071757589	-68.61911104	2.92084954	-68.619111	2.92084954
X Variable 2	-3.01178E+12	5.17674E+12	-0.581790555	0.561063017	-1.31914E+13	7.16783E+12	-1.319E+13	7.16783E+12
X Variable 3	3.01178E+12	5.17674E+12	0.581790555	0.561063017	-7.16783E+12	1.31914E+13	-7.168E+12	1.31914E+13
X Variable 4	3.01178E+12	5.17674E+12	0.581790555	0.561063017	-7.16783E+12	1.31914E+13	-7.168E+12	1.31914E+13
X Variable 5	-305.9520486	197.0356683	-1.552774943	0.121334135	-693.4056687	81.50157145	-693.40567	81.50157145
X Variable 6	-788.3763941	223.979269	-3.519863234	0.000485765	-1228.812277	-347.9405109	-1228.8123	-347.9405109
X Variable 7	-372.0414929	368.8347364	-1.008694291	0.313782243	-1097.323139	353.2401531	-1097.3231	353.2401531
X Variable 8	444.7719753	125.6569311	3.539573755	0.000452001	197.6784757	691.8654749	197.678476	691.8654749
X Variable 9	-1867.914181	671.9204735	-2.779963187	0.005714995	-3189.187746	-546.640616	-3189.1877	-546.640616
X Variable 10	505.2300069	173.6559329	2.909373716	0.003840941	163.7506167	846.7093972	163.750617	846.7093972
X Variable 11	1538.609189	673.1355968	2.285734399	0.022836178	214.9461892	2862.272189	214.946189	2862.272189

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

Pregnancy Test	Birth Control	Feminine Hygiene
Mean	0.075	Mean	0.14	Mean	0.141
Standard Error	0.008333333	Standard Error	0.010978184	Standard Error	0.011010915
Median	0	Median	0	Median	0
Mode	0	Mode	0	Mode	0
Standard Deviation	0.263523138	Standard Deviation	0.347160655	Standard Deviation	0.348195692
Sample Variance	0.069444444	Sample Variance	0.120520521	Sample Variance	0.12124024
Kurtosis	8.462662537	Kurtosis	2.323241804	Kurtosis	2.273689484
Skewness	3.231987188	Skewness	2.078123989	Skewness	2.066191206
Range	1	Range	1	Range	1
Minimum	0	Minimum	0	Minimum	0
Maximum	1	Maximum	1	Maximum	1
Sum	75	Sum	140	Sum	141
Count	1000	Count	1000	Count	1000
Confidence Level(95.0%)	0.016352845	Confidence Level(95.0%)	0.021542945	Confidence Level(95.0%)	0.021607174

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.

Folic Acid	Prenatal Vitamins	Prenatal Yoga
Mean	0.106	Mean	0.128	Mean	0.018
Standard Error	0.009739551	Standard Error	0.010570134	Standard Error	0.004206387
Median	0	Median	0	Median	0
Mode	0	Mode	0	Mode	0
Standard Deviation	0.307991654	Standard Deviation	0.334256979	Standard Deviation	0.133017644
Sample Variance	0.094858859	Sample Variance	0.111727728	Sample Variance	0.017693694
Kurtosis	4.581399453	Kurtosis	2.980162965	Kurtosis	50.83369238
Skewness	2.563638165	Skewness	2.230292947	Skewness	7.261682229
Range	1	Range	1	Range	1
Minimum	0	Minimum	0	Minimum	0
Maximum	1	Maximum	1	Maximum	1
Sum	106	Sum	128	Sum	18
Count	1000	Count	1000	Count	1000
Confidence Level(95.0%)	0.019112325	Confidence Level(95.0%)	0.020742211	Confidence Level(95.0%)	0.008254368

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.

Body Pillow	Ginger Ale	Sea Bands
Mean	0.018	Mean	0.069	Mean	0.03
Standard Error	0.004206387	Standard Error	0.008018934	Standard Error	0.005397141
Median	0	Median	0	Median	0
Mode	0	Mode	0	Mode	0
Standard Deviation	0.133017644	Standard Deviation	0.25358096	Standard Deviation	0.170672579
Sample Variance	0.017693694	Sample Variance	0.064303303	Sample Variance	0.029129129
Kurtosis	50.83369238	Kurtosis	9.62089868	Kurtosis	28.51261882
Skewness	7.261682229	Skewness	3.406121085	Skewness	5.518659029
Range	1	Range	1	Range	1
Minimum	0	Minimum	0	Minimum	0
Maximum	1	Maximum	1	Maximum	1
Sum	18	Sum	69	Sum	30
Count	1000	Count	1000	Count	1000
Confidence Level(95.0%)	0.008254368	Confidence Level(95.0%)	0.015735886	Confidence Level(95.0%)	0.010591033

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.

Stopped Buying Cigarettes	Cigarettes	Smoking Cessation Products
Mean	0.092	Mean	0.097	Mean	0.06
Standard Error	0.009144376	Standard Error	0.009363689	Standard Error	0.007513751
Median	0	Median	0	Median	0
Mode	0	Mode	0	Mode	0
Standard Deviation	0.289170572	Standard Deviation	0.296105857	Standard Deviation	0.237605674
Sample Variance	0.08361962	Sample Variance	0.087678679	Sample Variance	0.056456456
Kurtosis	6.006873157	Kurtosis	5.449903464	Kurtosis	11.79538488
Skewness	2.827518946	Skewness	2.727454425	Skewness	3.71103733
Range	1	Range	1	Range	1
Minimum	0	Minimum	0	Minimum	0
Maximum	1	Maximum	1	Maximum	1
Sum	92	Sum	97	Sum	60
Count	1000	Count	1000	Count	1000
Confidence Level(95.0%)	0.017944389	Confidence Level(95.0%)	0.018374755	Confidence Level(95.0%)	0.014744545

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. 

Stopped Buying Wine	Wine	Maternity Clothes	Pregnant
Mean	0.13	Mean	0.123	Mean	0.131	Mean	0.5
Standard Error	0.01064017	Standard Error	0.010391293	Standard Error	0.010674875	Standard Error	0.0158193
Median	0	Median	0	Median	0	Median	0.5
Mode	0	Mode	0	Mode	0	Mode	1
Standard Deviation	0.336471712	Standard Deviation	0.32860155	Standard Deviation	0.337569182	Standard Deviation	0.500250188
Sample Variance	0.113213213	Sample Variance	0.107978979	Sample Variance	0.113952953	Sample Variance	0.25025025
Kurtosis	2.862017051	Kurtosis	3.292766964	Kurtosis	2.804331821	Kurtosis	-2.004012036
Skewness	2.203700753	Skewness	2.299170594	Skewness	2.190599724	Skewness	-5.79052E-18
Range	1	Range	1	Range	1	Range	1
Minimum	0	Minimum	0	Minimum	0	Minimum	0
Maximum	1	Maximum	1	Maximum	1	Maximum	1
Sum	130	Sum	123	Sum	131	Sum	500
Count	1000	Count	1000	Count	1000	Count	1000
Confidence Level(95.0%)	0.020879646	Confidence Level(95.0%)	0.020391265	Confidence Level(95.0%)	0.020947749	Confidence Level(95.0%)	0.031042867

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.

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.672171463
R Square	0.451814475
Adjusted R Square	0.443457989
Standard Error	0.373195361
Observations	1000
ANOVA
df	SS	MS	F	Significance F
Regression	15	112.9536189	7.530241258	54.06751595	3.6614E-117
Residual	984	137.0463811	0.139274778
Total	999	250
Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	0.413672294	0.018939149	21.84217941	1.51822E-86	0.37650653	0.450838058	0.37650653	0.450838058
X Variable 1	0.218058401	0.046541505	4.685246063	3.18916E-06	0.12672639	0.309390412	0.12672639	0.309390412
X Variable 2	-0.274472752	0.034666709	-7.917473492	6.51371E-15	-0.342501929	-0.206443574	-0.342501929	-0.206443574
X Variable 3	-0.245079751	0.034344935	-7.13583383	1.86482E-12	-0.312477486	-0.177682016	-0.312477486	-0.177682016
X Variable 4	0.34405342	0.039239498	8.768038156	7.86842E-18	0.267050703	0.421056136	0.267050703	0.421056136
X Variable 5	0.298086596	0.036121626	8.252302888	4.95531E-16	0.227202323	0.368970869	0.227202323	0.368970869
X Variable 6	0.318919562	0.089495211	3.563537737	0.000383451	0.143296155	0.494542969	0.143296155	0.494542969
X Variable 7	0.184546094	0.089416384	2.063895744	0.039289318	0.009077376	0.360014811	0.009077376	0.360014811
X Variable 8	0.226086097	0.047063221	4.803880632	1.79836E-06	0.133730281	0.318441913	0.133730281	0.318441913
X Variable 9	0.140434527	0.069965526	2.007196064	0.045001874	0.00313574	0.277733315	0.00313574	0.277733315
X Variable 10	0.159818146	0.041818284	3.821728963	0.000140837	0.077754879	0.241881414	0.077754879	0.241881414
X Variable 11	-0.164941603	0.040425204	-4.080167477	4.86467E-05	-0.244271122	-0.085612084	-0.244271122	-0.085612084
X Variable 12	0.165721439	0.051586516	3.212495273	0.001358593	0.06448921	0.266953668	0.06448921	0.266953668
X Variable 13	0.191146551	0.03600652	5.308664995	1.365E-07	0.12048816	0.261804943	0.12048816	0.261804943
X Variable 14	-0.207701535	0.036766736	-5.649169753	2.10997E-08	-0.279851759	-0.135551311	-0.279851759	-0.135551311
X Variable 15	0.240508913	0.035811449	6.715978306	3.15411E-11	0.170233324	0.310784502	0.170233324	0.310784502

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.

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