Statistics: Analysis of Website Visits and Sales for QA Company
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This report analyzes the website visits and sales data for a QA company. It provides insights into the trends and patterns observed in the data, and makes recommendations for improving sales.
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Statistics Business intelligence and data visualization Student name: Tutor name: 1|P a g e
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Statistics Executive summary Similar to some other enterprise QA company in its operations faces competition from different corporations which might be producing similar products. So as to grow its sales through widening its market, it established an internet site in which customers would learn more about the organization. Diverse records have been kept about what took place in the internet site over a time frame. These records were analyzed and presented in form of tables and graphs. The consequences confirmed that variety of specific visits to the website become excessive between February and March. However, sales and the wide variety of pounds bought over the period seemed not to enhance. This become glaring as earnings have been handiest excessive for the duration of the pre-promotion length and then it decreased. The research recommends to the management of QA to give you other marketing avenues aside from the website to power income. They must also allow customers to location orders thru the website as the research has installed that there are many individuals who visit the website. 2|P a g e
Statistics QUESTION ONE Descriptive statistics Unique visits to the QA website per week May 25 - May 31 Jun 15 - Jun 21 Jul 6 - Jul 12 Jul 27 - Aug 2 Aug 17 - Aug 23 Sep 7 - Sep 13 Sep 28 - Oct 4 Oct 19 - Oct 25 Nov 9 - Nov 15 Nov 30 - Dec 6 Dec 21 - Dec 27 Jan 11 - Jan 17 Feb 1 - Feb 7 Feb 22 - Feb 28 Mar 15 - Mar 21 Apr 5 - Apr 11 Apr 26 - May 2 May 17 - May 23 Jun 7 - Jun 13 Jun 28 - Jul 4 Jul 19 - Jul 25 Aug 9 - Aug 15 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 Figure 1. Unique Visits to the QA website per week Figure 1 The bar graph above shows the frequency of unique visits to QA website per week from May 25th2008 to August 15th2009. It can be observed that the number of unique visits to the website was high between February 1stand April 11th. This is after a decline was observed from May 25th to around December 21st2008. To add on, another decline in the number of visits to the website starts from February 22nd2009 to 15thAugust 2009. Revenue over time per week 3|P a g e
Statistics May 25 - May 31 Jun 22 - Jun 28 Jul 20 - Jul 26 Aug 17 - Aug 23 Sep 14 - Sep 20 Oct 12 - Oct 18 Nov 9 - Nov 15 Dec 7 - Dec 13 Jan 4 - Jan 10 Feb 1 - Feb 7 Mar 1 - Mar 7 Mar 29 - Apr 4 Apr 26 - May 2 May 24 - May 30 Jun 21 - Jun 27 Jul 19 - Jul 25 Aug 16 - Aug 22 $0 $200,000 $400,000 $600,000 $800,000 $1,000,000 Figure 2. Revenue over time per week Figure 2 The figure above is of graph of revenue got by QA per week over a period of time. as can be observed, no particular pattern can be seen from the frequencies. It can be said that the frequencies for the revenue per week is relatively uniform throughout the period. Profit over time per week May 25 - May 31 Jun 22 - Jun 28 Jul 20 - Jul 26 Aug 17 - Aug 23 Sep 14 - Sep 20 Oct 12 - Oct 18 Nov 9 - Nov 15 Dec 7 - Dec 13 Jan 4 - Jan 10 Feb 1 - Feb 7 Mar 1 - Mar 7 Mar 29 - Apr 4 Apr 26 - May 2 May 24 - May 30 Jun 21 - Jun 27 Jul 19 - Jul 25 Aug 16 - Aug 22 $0 $50,000 $100,000 $150,000 $200,000 $250,000 $300,000 Fig. 3. Profit over time per week Figure 3 The figure above is of graph of profit got by QA per week over a period of time. As can be observed, there is a general decline of the profits from the beginning of the period towards the end of the period. However, the profits seem to pick up from June 19th2009 towards the end. 4|P a g e
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Statistics Pounds over time per week May 25 - May 31 Jun 22 - Jun 28 Jul 20 - Jul 26 Aug 17 - Aug 23 Sep 14 - Sep 20 Oct 12 - Oct 18 Nov 9 - Nov 15 Dec 7 - Dec 13 Jan 4 - Jan 10 Feb 1 - Feb 7 Mar 1 - Mar 7 Mar 29 - Apr 4 Apr 26 - May 2 May 24 - May 30 Jun 21 - Jun 27 Jul 19 - Jul 25 Aug 16 - Aug 22 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 Fig. 4. Lbs. Sold overtime per week Figure 4 The figure above is of graph of pounds sold by QA per week over a period of time. As can be observed, there is a no any particular pattern over the period. The number of pounds sold over the period can be said to be uniformly distributed. QUESTION TWO Visit and financial summary measures – initial period Visits Unique visitsRevenueProfit Lbs. Sold Mean1,055976608,250200,23318,737 Median899846586,170208,91317,270 Std. dev 355.033 3 319.597213 9 155930.39 7 60691.5 5 5427.39 3 Minimu m626594274,56862,5808,633 Maximu m1,6321,509890,077275,21828,053 Table 1 Visit and financial summary measures – Pre-promotion period 5|P a g e
Statistics Visits Unique visitsRevenueProfit Lbs. Sold Mean563517534,314159,93218,441 Median558510534,542152,47617,215 Std. dev 80.8711 8 70.9391422 6 150502.8 2 42682.6 8 5965.6 3 Minimu m383366315,647100,3888,992 Maximu m795734951,216273,17531,969 Table 2 Visit and financial summary measures – Promotion period Visits Unique visitsRevenueProfit Lbs. Sold Mean1,8141,739456,399131,93017,113 Median1,6631,585413,937114,32817,299 Std. dev 758.101 7743.025339 161741.09 9 47776.8 5 6519.07 1 Minimu m1,000930268,16081,8417,814 Maximu m3,7263,617897,164266,47731,496 Table 3 Visit and financial summary measures – Post promotion period Visits Unique visitsRevenueProfit Lbs. Sold Mean857801371,728111,04614,578 Median848800348,397104,53013,647 Std. dev 70.8885 5 72.3561475 5 145728.33 4 49065.2 7 5941.55 6 Minimu m772709133,96732,8253,826 Maximu m963912615,950206,44123,762 Table 4 6|P a g e
Statistics QUESTION THREE The means of visits, unique visits, revenue, profits and pounds sold for four periods Means Visits Unique visits Revenu eProfits Lbs. Sold Initial1,055976608,250200,23318,737 Pre- promotion 562.952 4 516.809523 8 534313. 5159932 18440.7 7 Promotion1,8141,739456,399131,93017,113 Post- proportion 856.571 4 800.785714 3371728 111045. 8 14577.7 9 Table 5 QUESTION FOUR Summary of findings in 2 and 3 The table 1 above shows the results of summary statistics for visits, unique visits, revenue, profits and pounds sold for the initial period. The mean, median, standard deviation, minimum and maximum values for visits per week were 1055, 899, 355.03, 626 and 1632 respectively. The mean, median, standard deviation, minimum and maximum values for unique visits per week were 976, 846, 319.597, 694 and 1509 respectively. The mean, median, standard deviation, minimum and maximum values for revenue per week were 608250, 586170, 155930.397, 274568and890077respectively.Themean,median,standarddeviation,minimumand maximum values for profit per week were 200233, 208913, 60691.55, 62580 and 275218 respectively. Lastly, the mean, median, standard deviation, minimum and maximum values for pounds sold per week were 18737, 17270, 5427.39, 8633 and 28053 respectively. The table 2 above shows the results of summary statistics for visits, unique visits, revenue, profits and pounds sold for the pre-promotion period. The mean, median, standard deviation, minimum and maximum values for visits per week were 553, 558, 80.87, 383 and 795 respectively. The mean, median, standard deviation, minimum and maximum values for unique visits per week were 517, 510, 70.93, 366 and 734 respectively. The mean, median, standard deviation,minimumandmaximumvaluesforrevenueperweekwere534314,534542, 7|P a g e
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Statistics 150502.82, 315642, and 951216 respectively. The mean, median, standard deviation, minimum and maximum values for profit per week were 159932, 152476, 42682.68, 100388 and 273175 respectively. Lastly, the mean, median, standard deviation, minimum and maximum values for pounds sold per week were 18441, 17215, 5965.63, 8992 and 31969 respectively. The table 3 above shows the results of summary statistics for visits, unique visits, revenue, profits and pounds sold for the promotion period. The mean, median, standard deviation, minimum and maximum values for visits per week were 1814, 1663, 758.10, 1000 and 3726 respectively. The mean, median, standard deviation, minimum and maximum values for unique visits per week were 1739, 1585, 743.02, 930 and 3617 respectively. The mean, median, standard deviation, minimum and maximum values for revenue per week were 456399, 413937, 161741.099, 268160, and 897164 respectively. The mean, median, standard deviation, minimum and maximum values for profit per week were 131930, 114328, 47776.85, 81841 and 266477 respectively. Lastly, the mean, median, standard deviation, minimum and maximum values for pounds sold per week were 17113, 17229, 6519.07, 7814 and 31496 respectively. The table 4 above shows the results of summary statistics for visits, unique visits, revenue, profits and pounds sold for the post promotion period. The mean, median, standard deviation, minimum and maximum values for visits per week were 857, 848, 70.89, 772 and 963 respectively. The mean, median, standard deviation, minimum and maximum values for unique visits per week were 801, 800, 72.35, 709 and 912 respectively. The mean, median, standard deviation,minimumandmaximumvaluesforrevenueperweekwere371728,348397, 145728.33, 133967 and 615950 respectively. The mean, median, standard deviation, minimum and maximum values for profit per week were 111046, 104530, 49065.27, 32825 and 206441 respectively. Lastly, the mean, median, standard deviation, minimum and maximum values for pounds sold per week were 14578, 13647, 5941.56, 3826 and 23762 respectively. It can be observed from table 5 that the mean number of visits during the initial, pre-promotion, promotion and post promotion periods were 1055, 562.95, 1814 and 856.57. The mean number of unique visits during the initial, pre-promotion, promotion and post promotion periods were 976, 516.81, 1739 and 800.79. The mean number of revenue during the initial, pre-promotion, promotion and post promotion periods were 608250, 53431.5, 456339 and 371728. The mean number of profits during the initial, pre-promotion, promotion and post promotion periods were 200233, 159932, 131930 and 111045.8. Lastly, the mean number of pounds sold during the initial, pre-promotion, promotion and post promotion periods were 18737, 18440.77, 17113 and 14577.79 RELATIONSHIP BETWEEN VARIABLES QUESTION FIVE 8|P a g e
Statistics Correlation coefficient between revenue and pounds sold. Revenu e Lbs. Sold Revenue1 Lbs. Sold 0.868931 Table 6 From my intuition, there is a positive relationship between revenue and pounds sold. This is because any number of pounds sold can only increase the amount of revenue and not decrease. Scatterplot of revenue versus pounds sold 05,00010,00015,00020,00025,00030,00035,000 $0 $100,000 $200,000 $300,000 $400,000 $500,000 $600,000 $700,000 $800,000 $900,000 $1,000,000 f(x) = 24.5679059575216 x + 69380.9472593738 R² = 0.755038845893767 Scatterplot of revenue vs pounds sold Pounds sold Revenue Figure 5 The correlation coefficient r, between revenue and pounds sold is 0.87. This indicates that there is a strong and positive relationship between the two variables. The scatterplot above also depicts a linear relationship with a high value of R-squared (0.76). Question six Correlation of revenue versus visits VisitsRevenue Visits1 9|P a g e
Statistics Revenu e -0.05931 Table 7 Scatterplot of revenue versus visits 05001,0001,5002,0002,5003,0003,5004,000 $0 $100,000 $200,000 $300,000 $400,000 $500,000 $600,000 $700,000 $800,000 $900,000 $1,000,000 f(x) = − 15.970543633977 x + 512241.046258343 R² = 0.00352738952999587 Scatterplot of revenue vs visits Visits Revenue Figure 6 I expect this plot to be positively linear. However, it is linear but not positive. The value of correlation coefficient is -0.06. This indicates a very weak linear relationship in the negative direction. Number seven Summary of results The correlation coefficient r, between revenue and pounds sold is 0.87. This indicates that there is a strong and positive relationship between the two variables. The scatterplot above also depicts a linear relationship with a high value of R-squared (0.76). The relationship between visits and revenue were expected to be positive, however, it is linear but not positive. The value of correlation coefficient is -0.06. This indicates a very weak linear relationship in the negative direction. 10|P a g e
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Statistics Number eight a.Summary statistics for Lbs. sold from January 3, 2015 to January 19, 2010 Results table Lbs. Sold Mean 18681.5551 7 Median17673 Standard Deviation6840.50794 Minimum3826 Maximum44740 Table 8 The summary statistics for pounds sold is as shown in the table above. The mean Lbs sold was 18681.56 while the median and the standard deviation were 17,673 and 6840.51. The minimum and maximum number of Lbs sold was 3826 and 44740 respectively. b.Histogramfor Lbs. sold from January 3, 2015 to January 19, 2010 11|P a g e
Statistics Figure 7 c.It can be observed that the Lbs. sold distribution is bell-shaped. The distribution depicts a perfect normal distribution. d.The empirical rule table Table of results Interval Theoretical % of data Theoretical no. of observations Actual no. of observations Mean ± 1 std dev68%197201 Mean ± 2 std dev95%276276 Mean ± 3 std dev99%287288 Table 9. The values in the table have been rounded off to nearest whole number. e.Refined analysis Interval Theoretical no. of obsActual no. of obs Mean - 1 std dev99117 Mean + 1 std dev9984 1 std dev to 2 std dev13835 -1 std dev to .-2 std dev13840 12|P a g e
Statistics 2 std dev to 3 std dev14411 -2 std dev to -3 std devn1443 Table 10 f.How well does the data follow normal distribution can be determined by observing the number of actual observations within the negative and the positive intervals. For example how many actual observations are in the interval between 1 standard deviation and 2 standard deviations? These numbers should be equal on both sides of the symmetry of the normal curve. However, in this case, the numbers are not equal. For example 11 and 3 for the interval between 2 standard deviation and 3 standard deviations positive side and negative side respectively. So I can conclude in this respect that the data is not perfectly normally distributed. g.Skewness and kurtosis of the data Table of results Lbs. Sold Mean18681.55517 Standard Error401.6884574 Median17673 Mode14905 Standard Deviation6840.50794 Sample Variance46792548.87 Kurtosis0.563661442 Skewness0.632307485 Range40914 Minimum3826 Maximum44740 Sum5417651 Count290 Table 11 13|P a g e
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Statistics From the summary statistics above, we can see that the skewness value is 0.63. This indicates that the data is positively skewed. It has a longer tail to the right. However, it is not much skewed since 0.63 is close to 0 (zero). When it comes to kurtosis, we can see that its value is 0.56, this means that the distribution is platykurtic; the tail of the distribution is short and thin. To add on, the distribution has a lower and broader peak. Question nine Comparison of the pounds sold and daily visit distribution From the summary statistics of the visits attached, we can see that the skewness value is 2.17 compared to that of pounds sold which is 0.63. This indicates that the data of visits is more positively skewed than that of pounds sold. It has a longer tail to the right than the data of pounds sold. Thus we conclude that the distribution of visits data is so much skewed to the right than the distribution of pounds sold.When it comes to kurtosis, we can see that the kurtosis for number of visits is 5.86 compared to 0.56 for number of pounds. This means that the distribution visits is more kurtic than that of pounds sold. Its curve has a higher and sharper peak. Question ten Graph of distribution of visits by traffic source Referring SitesSearch EnginesDirect TrafficOther 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 38,754 20,964 9,709 4 Distribution of visits by traffic source Figure 8 14|P a g e
Statistics The graph above shows the distribution of visits by traffic source. It can be observed that most traffic came from referrals which were 38,754. The least number came from direct traffic which was 9709. Only 4 sources were not specified. Graph of distribution of visits by referring sites googleads.g.doubleclick.net pagead2.googlesyndication.com sedoparking.com globalspec.com freepatentsonline.com thomasnet.com mu.com mail.google.com psicofxp.com 0 4,000 8,000 12,000 16,00015,626 8,044 3,138 693582389379344337310 Distribution of visits by referring sites Figure 9 15|P a g e
Statistics The graph above shows the distribution of visits by referral sites. It can be observed that most referralsitesweregoogleads.doubleclick.net(15,626)whiletheleastreferralssiteswere psicofx.com (310). Graph of distribution of visits by search engine googleyahoosearchmsnaolasklivebingvoilanetscape 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,00017,681 1,2505924243092681451226326 Distribution of visits by search engine Figure 10 The graph above shows the distribution of visits by search engine. It can be observed that most used search engine was google (17,681) while the least used search engine was netscape (26). Graph of distribution of visits by geographic sources 16|P a g e
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Statistics South America Northern America Central America Western Europe Eastern Asia Northern Europe Southern Asia South-Eastern Asia Southern Europe Eastern Europe 0 5,000 10,000 15,000 20,000 25,00022,616 17,509 6,7765,2143,2282,7212,5891,9681,5381,427 Distribution of visits by Geographic Sources Figure 11 The graph above shows the distribution of visits by geographic sources. It can be observed that most visits came from South America (22,616) while the least visits came from Eastern Europe (1,427). Graph of distribution of visits by top 10 browsers 17|P a g e
Statistics Internet Explorer Firefox Opera Safari Chrome Mozilla Netscape Konqueror SeaMonkey Camino 0 10,000 20,000 30,000 40,000 50,000 60,00053,080 13,142 9388507924784731249 Distribution of visits by browser type Figure 11 The graph above shows the distribution of visits by browser type. It can be observed that most used browser was internet explorer (53,080) while the least used browser was Camino (9). Graph of distribution of visits by top 10 operating systems used. Windows Macintosh Linux (not set) iPhone SymbianOS FreeBSD iPod Playstation 3 Playstation Portable 0 20,000 40,000 60,000 80,00067,063 1,1841,04548292018843 Distribution of visits by operating system used Figure 12 18|P a g e
Statistics The graph above shows the distribution of visits by operating system used. It can be observed that most used operating system was windows (67,063) while the least used operating system was playstation portable (3). 19|P a g e