Abstract
This is analysis report of the number of customers who entered a small hardware shop each day of the week for 40 weeks. A total of 6409 customers visited the hardware for the entire duration. Saturdays recorded 2005 visitors which was the highest while Wednesdays recorded 406 which was the least. Together, the last three days, Thursday, Friday, and Saturday accounted for 71% of the total number of customers. On average about 26 customers visited the hardware per week. The trend line graph showed that the number of customers who visited the hardware on Mondays, Tuesdays, and Wednesday increased while those who visited on Fridays and Saturdays decreased. There were no significant changes in the number of customers who visited the hardware on Saturdays. Moreover, the number of customers who visited the hardware declined as the week progressed.
General Analysis
The following table gives a summary of the number of customers who visited the hardware in 40 weeks
On average a total of 160 customers visited the hardware every week. The least number of customers were registered in week 26th week while the highest number visited on 29th week. A total of 6409 customers visited the hardware in 40 weeks, with majority 2005 visiting on Saturday while minority 496 visited on Wednesday.
The following graph shows the general trend for customer visits per week. The above graph show that the number of customers who visited the hardware decreased every week from about 175 in the beginning to about 150 at the end of 40 weeks. This was the general trend displayed by the scatter plot. Scatter plots are used to show the nature and degree of association between variables (Mulekar & Princeton Review, 2004).The graph depicts a linear relationship between the number of customers and the week. It shows that the correlation between number of customers and week is negative.
The following graph shows the general trend for customer visits per day
The number of who visited the hardware on Mondays, Tuesdays, and Wednesday increased every week. Those who visited on Thursday and Fridays declined as the weeks progressed. On the other hand, the number customers who came to the hardware on Saturdays remained almost at the same level.
The following pie chart show proportion of the number of customers per day
The above pie chart was used to show the proportion of customers who visited the hardware on specific days. A pie chart is used to show relationship among components of a whole and it is good for comparison purposes (Chaturvedi, & Chaturvedi, 2011).The pie chart above show that majority of the customers visited the hardware on Saturdays. The number of customers on this day accounted for 31% of the total customers. It was followed by Friday which accounted for 24% of the total number of customers. Many customers, 71% visited the hardware on Thursday, Friday and Saturday. Least number of customers, 8% came to the hardware on Wednesdays and Mondays.
The following table shows the average number of customers per day and their standard deviations
An average of about 50 customers visited the hardware on Saturdays followed by 38 on Fridays and 25 on Thursdays. The average number of customers visiting on Mondays and Wednesday were equal. However, there was a wide variation on Wednesdays than on Mondays. Wednesday had a standard deviation of 6 customers while Monday had 3 customers. This implies that there were some Wednesdays that registered extreme values in the number of customers. A standard deviation is a measure of variability of a given set of data (Anderson, Sweeney & Williams, 2009). Thursdays had the highest standard deviation of 17 customers which was more than half of the average number of customers. This shows that it had the most unpredictable number of customer visits. At times the number will be very low or very high.
The following graph shows the average number of customers per day
Bar graphs are used to comparison of values of a given quantity with other quantities (Goozner, 2001). The above graph shows that the number of customers who visited the hardware increased from Monday to Saturdays with exception of Wednesday. This implies that the business starts at low pace at the beginning of the week and picks up in the middle of the week.
The is a line graph showing the number of customers per week for each day
Line graphs are appropriate when the trend displayed by a given quantity over time is required (Kiernan, 2001). The above graph shows there were positive and negative fluctuations in the of customer visits per day.
Analysis for Days
Mondays
A total of 507 customers visited the hardware on Mondays. This was 8% of the total number of customers 40 weeks. On average, 12 customers visited the hardware on this day. The standard deviation for this day was 3 customers. Meaning, there were between 9 and 15 customers who visited the hardware on Mondays at 68% confidence level. The general trend was the number of customers who came on Mondays increased as the weeks progressed. Tuesdays
A total of 862 customers visited the hardware on Tuesdays. This was 13% of the total number of customers 40 weeks. On average, 21 customers visited the hardware on this day. The standard deviation for this day was 7 customers. Meaning, there were between 14 and 28 customers who visited the hardware on Tuesdays at 68% confidence level. The general trend was the number of customers who came on Tuesdays increased as the weeks progressed.
Wednesdays
A total of 496 customers visited the hardware on Wednesdays. This was 8% of the total number of customers 40 weeks. On average, 12 customers visited the hardware on this day. The standard deviation for this day was 6 customers. Meaning, there were between 6 and 18 customers who visited the hardware on Wednesdays at 68% confidence level. The general trend was the number of customers who came on Wednesdays increased as the weeks progressed
Thursdays
A total of 1016 customers visited the hardware on Thursdays. This was 16% of the total number of customers 40 weeks. On average, 25 customers visited the hardware on this day. The standard deviation for this day was 17 customers. Meaning, there were between 8 and 42 customers who visited the hardware on Thursdays at 68% confidence level. The general trend was the number of customers who came on Thursdays decreased as the weeks progressed
Fridays
A total of 123 customers visited the hardware on Fridays. This was 24% of the total number of customers 40 weeks. On average, 38 customers visited the hardware on this day. The standard deviation for this day was 6 customers. Meaning, there were between 32 and 44 customers who visited the hardware on Fridays at 68% confidence level. The general trend was the number of customers who came on Fridays decreased as the weeks progressed
Saturdays
A total of 2005 customers visited the hardware on Mondays. This was 31% of the total number of customers 40 weeks. On average, 50 customers visited the hardware on this day. The standard deviation for this day was 7 customers. Meaning, there were between 43 and 57 customers who visited the hardware on Saturdays at 68% confidence level. The general trend was the number of customers who came on Saturdays did not change significantly as week progressed.
An Investigation of Activity on Wednesdays
The following is a line graph showing the number of customers per week on Wednesdays
The following is a histogram of the frequency of visits per week on Wednesdays
The general trend for Wednesday as discussed in the days’ analysis sections was that the number of customers increased as the weeks progressed. This trend is shown by both the line graph and the histogram. The initial weeks 1st to 20th recorded low number of customer visits. Many customers visited the hardware from 21st to 40th week. These final weeks accounted for 64% of the total number of customers on Wednesdays
Analysis of the Independence of Tuesdays and Thursday
The following is a line graph of the number of customers per week on Tuesdays and Thursdays
The above graphs show that the trend for Tuesdays was almost similar to the trend on Thursdays but in the opposite direction. The two graphs are approximate reflection of each other. From the graph it can be deduced that whenever the number of customer increased on a particular Tuesday of the week, it was most likely that the Thursday’s number for the same week would reduce by almost similar margin. Therefore, the two variables were dependent.
A regression analysis for the two variables shows that they have strong negative correlation coefficient of 0.72. Besides, 52% variability on Tuesdays is attributed to the relationship between the two variables.
Recommendations
It is worth mentioning, that the hardware business did not perform well during the 40 weeks under review. This is because the general trend was that the number of customers who visited the hardware deceased as the weeks progressed. This trend was notable on Fridays and Thursdays. Again, the average number of customers who visited the hardware increased from Mondays towards Saturdays. For this reason, it is recommended that the number of staff be reduced on Mondays, Tuesdays, and Wednesdays in the short term.
References
Anderson, D. R., Sweeney, D. J., & Williams, T. A. (2009). Essentials of modern business statistics with Microsoft Office Excel. Mason, OH: South-Western.
Chaturvedi, P. D., & Chaturvedi, M. (2011). Business communication: Concepts, cases and applications. Delhi: Dorling Kindersley.
Goozner, C. (2001). Business math the easy way. Hauppauge, N.Y: Barron's.
Kiernan, D. (2001). Great, graphs, charts & tables that build real-life math skills. New York: Scholastic Professional Books.
Mulekar, M. S., & Princeton Review (Firm). (2004). Cracking the AP statistics exam. New York, NY: Random House.
