# Exploring Numbers

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MethodologyThe population consists of all the organizations in the world. A method of simple randomsampling is used for the purpose of Data Collection. A total of 200 companies are randomlyselected. Each of these companies are asked regarding the average number of products soldper month, average communication time between the potential customer and the businessprovider per day, the average number of customers reached with the help of social media perday, the average number of SMS sent, the total cost on mobile and their profit margin on theproduct.DataA dependent variable is the total number of products sold. This measures the productivity ofan organization. The independent variables are communication time (hours/day), the totalnumber of customers reached by social media, SMS sent, cost on mobile, and profit margin inpercentage. It is expected that the communication time, customers read is social media andSMS sent should increase the productivity of the organization. However, the cost of themobile is expected to have a negative relationship with the productivity of the organization.AnalysisRegression AnalysisThe regression results are given below.SUMMARY OUTPUTRegression StatisticsMultiple R0.999997R Square0.999994Adjusted R Square0.999994Standard Error0.593598
Observations200ANOVAdfSSMSFSignificance FRegression511883421237668467450810Residual19468.357480.352358Total19911883489CoefficientsStandardErrort StatP-valueLower 95%Upper95%Intercept-0.793370.222951-3.558510.000469-1.23309-0.35365communication time (hours/day)0.005090.0169760.2998140.76464-0.028390.038571Customers reached by social media0.0009973.14E-06318.00281.3E-2650.0009910.001004SMS sent0.0012460.0015750.7914780.429632-0.001860.004352cost on mobile0.0003940.0003131.2594470.209382-0.000220.001011profit margin (%)-0.02840.014412-1.970810.050168-0.056832.1E-05The Null hypothesis, the model is not significant. Versus the alternative hypothesis that themodel is significant. With F=6745080, P<5%, the null hypothesis is rejected at 5% level ofsignificance. There is sufficient evidence to conclude the significance of the model.There is 99% variation in the total number of products sold which is explained bycommunication time (hours/day), Customers reached by social media, SMS sent, cost onmobile and profit margin (%). This percentage is a good and fitted model is said to be a goodfit for the dataThe regression equation is given by: product sold =-0.79337 + 0.005*communication time(hours/day) +0.00099*Customers reached by social media +0.0012*SMS sent+0.00039*cost on mobile-0.0284*profit margin (%)With one hour per day increases in the communication time between potential customer andbusiness, the total number of product sold is increased by 0.005. But this value is notsignificant at 5% level of significance with t=0.299, p>5%.
With an increase in one customer reached by social media, the total number of product sold isincreased by 0.0009. This value is significant at 5% level of significance with t=318, p<5%.With one SMS sent increase, the total number of product sold is increased by0.0012. Thisvalue is not significant at 5% level of significance with t=0.791, p>5%.With \$1 increases in the cost of mobile, the total number of product sold is increasedby0.00039. This value is not significant at 5% level of significance with t=1.259, p>5%.With an increase in 1% profit margin, the total number of product sold is decreased by 0.028this value is not significant at 5% level of significance with t=-1.97, p>5%.Correlation AnalysisThe table of correlation is given below.Productssoldcommunication time(hours/day)Customersreached bysocialmediaSMSsentcostonmobileprofitmargin (%)Products sold1communication time (hours/day)-0.05161Customers reached by social media0.999997-0.051561SMS sent0.998476-0.056360.9984711cost on mobile-0.015850.532425-0.01567-0.021531profit margin (%)-0.04910.468692-0.04881-0.053260.9049751

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