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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 outcomesoriented 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 socioeconomic 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.
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 middleaged 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 2630% 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 3033 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 
Pvalue 
Lower 95% 
Upper 95% 
Lower 95.0% 
Upper 95.0% 

Intercept 
7492.402332 
4814508734 
1.55621E06 
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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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 noncomplex 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 machinelearning 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.
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.
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 selfreflection on the various requirements of a data analytics project shall be further briefly discussed in this report.
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 
5.79052E18 
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 
3.6614E117 

Residual 
984 
137.0463811 
0.139274778 

Total 
999 
250 

Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 
Lower 95.0% 
Upper 95.0% 

Intercept 
0.413672294 
0.018939149 
21.84217941 
1.51822E86 
0.37650653 
0.450838058 
0.37650653 
0.450838058 
X Variable 1 
0.218058401 
0.046541505 
4.685246063 
3.18916E06 
0.12672639 
0.309390412 
0.12672639 
0.309390412 
X Variable 2 
0.274472752 
0.034666709 
7.917473492 
6.51371E15 
0.342501929 
0.206443574 
0.342501929 
0.206443574 
X Variable 3 
0.245079751 
0.034344935 
7.13583383 
1.86482E12 
0.312477486 
0.177682016 
0.312477486 
0.177682016 
X Variable 4 
0.34405342 
0.039239498 
8.768038156 
7.86842E18 
0.267050703 
0.421056136 
0.267050703 
0.421056136 
X Variable 5 
0.298086596 
0.036121626 
8.252302888 
4.95531E16 
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 
1.79836E06 
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 
4.86467E05 
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 
1.365E07 
0.12048816 
0.261804943 
0.12048816 
0.261804943 
X Variable 14 
0.207701535 
0.036766736 
5.649169753 
2.10997E08 
0.279851759 
0.135551311 
0.279851759 
0.135551311 
X Variable 15 
0.240508913 
0.035811449 
6.715978306 
3.15411E11 
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.
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, RealTime 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, SeaBands, 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 Selfservice 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.
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.262270.
Be?o, M., 2021. The advantages and disadvantages of Eworking: An examination using an ALDINE analysis. Emerging Science Journal, 5, pp.112
Chaim, P. and Laurini, M.P., 2018. Volatility and return jumps in bitcoin. Economics Letters, 173, pp.158163.
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(910), pp.20522061.
Prabheesh, K.P., Padhan, R. and Garg, B., 2020. COVID19 and the oil price–stock market nexus: Evidence from net oilimporting 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.139145.
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 nickelbased superalloys. Acta Materialia, 178, pp.4558.
Anshari, M., Almunawar, M.N., Lim, S.A. and AlMudimigh, A., 2019. Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), pp.94101.
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.211216.
Chen, H., ChanOlmsted, 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.2841.
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.6679.
McCarthy, R.V., McCarthy, M.M., Ceccucci, W., Halawi, L., McCarthy, R.V., McCarthy, M.M., Ceccucci, W. and Halawi, L., 2019. Applying Predictive Analytics (pp. 89121). Cham: Springer International Publishing.
Nusraningrum, D. and Gading, D.K., 2021. Purchase Intention of Pregnancy Pillow: Price, Brand Awareness, and Brand Image. PSYCHOLOGY AND EDUCATION, 58(2), pp.45364550.
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 timebounded purchases during a mega shopping festival. In 2019 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 18). 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. 4147).
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