Difference between revisions of "Teppei Syokudo - Improving Store Performance: ESK Data Analysis Methodology"
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<i><b>Time of Day Effect.</b></i> We were provided with hourly and daily data for both sales and labour within a three month period. In exploring the data given, we found that there was a pattern in the sales transactions. | <i><b>Time of Day Effect.</b></i> We were provided with hourly and daily data for both sales and labour within a three month period. In exploring the data given, we found that there was a pattern in the sales transactions. | ||
| − | [[File:Reg-figure2.png|500px]] | + | <p>[[File:Reg-figure2.png|500px]]</p> |
<p>Mean sales per hour peaked from 11:00 to 13:00 and from 18:00 to 20:00. We attribute the peak periods to the lunchtime and dinnertime crowds respectively. Other time periods are idle time. We realise that this time of day effect will have to be taken into account when evaluating labour productivity in our hypotheses so that sales toward a particular factor would not be over or under attributed. We label 11:00 to 13:00 as Lunch Peak, 14:00 to 17:00 as Idle, and 18:00 to 20:00 as Dinner Peak.</p> | <p>Mean sales per hour peaked from 11:00 to 13:00 and from 18:00 to 20:00. We attribute the peak periods to the lunchtime and dinnertime crowds respectively. Other time periods are idle time. We realise that this time of day effect will have to be taken into account when evaluating labour productivity in our hypotheses so that sales toward a particular factor would not be over or under attributed. We label 11:00 to 13:00 as Lunch Peak, 14:00 to 17:00 as Idle, and 18:00 to 20:00 as Dinner Peak.</p> | ||
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<i><b>Day of Week Effect.</b></i> We also found the mean sales for every day of the week. Accounting for the time of day effect, we find the mean sales each day of the week. The day of the week also accounts for public holidays as crowds may be higher during public holidays. Similar to the time of day effect, the chart below shows that there is also a day of week effect. | <i><b>Day of Week Effect.</b></i> We also found the mean sales for every day of the week. Accounting for the time of day effect, we find the mean sales each day of the week. The day of the week also accounts for public holidays as crowds may be higher during public holidays. Similar to the time of day effect, the chart below shows that there is also a day of week effect. | ||
| − | [[File:Reg-figure3.jpg|500px]] | + | <p>[[File:Reg-figure3.jpg|500px]]</p> |
<p>During Lunch Peak and Dinner Peak, the store tends to achieve higher sales average on Fridays, with Saturdays experiencing lower mean sales. However during Idle periods, Saturdays and Public Holidays tend to experience relatively better mean sales compared to Idle periods on other days in the week.</p> | <p>During Lunch Peak and Dinner Peak, the store tends to achieve higher sales average on Fridays, with Saturdays experiencing lower mean sales. However during Idle periods, Saturdays and Public Holidays tend to experience relatively better mean sales compared to Idle periods on other days in the week.</p> | ||
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Revision as of 13:24, 17 April 2016
Data Exploration
Time of Day Effect. We were provided with hourly and daily data for both sales and labour within a three month period. In exploring the data given, we found that there was a pattern in the sales transactions.
Mean sales per hour peaked from 11:00 to 13:00 and from 18:00 to 20:00. We attribute the peak periods to the lunchtime and dinnertime crowds respectively. Other time periods are idle time. We realise that this time of day effect will have to be taken into account when evaluating labour productivity in our hypotheses so that sales toward a particular factor would not be over or under attributed. We label 11:00 to 13:00 as Lunch Peak, 14:00 to 17:00 as Idle, and 18:00 to 20:00 as Dinner Peak.
Day of Week Effect. We also found the mean sales for every day of the week. Accounting for the time of day effect, we find the mean sales each day of the week. The day of the week also accounts for public holidays as crowds may be higher during public holidays. Similar to the time of day effect, the chart below shows that there is also a day of week effect.
During Lunch Peak and Dinner Peak, the store tends to achieve higher sales average on Fridays, with Saturdays experiencing lower mean sales. However during Idle periods, Saturdays and Public Holidays tend to experience relatively better mean sales compared to Idle periods on other days in the week.
Removing Autocorrelation. We test for autocorrelation using the Durbin-Watson test. Taking the example for testing the effect of manager presence on sales per customer, we ran a Fit Model using Y as Sales/Customer Number and Model Effects using Total number of Manager Labour Hours.
The results of the Durbin-Watson test is shown below:
With p-value less than 0.05, we know that autocorrelation is present in our data. The Durbin-Watson value of 1.51 tells us that there is some positive correlation between Sales/Customer Number and Total number of Manager Labour Hours.
To account for the time of day and day of week effects, in the Fit Model, we add Day and Lunch Peak/Idle/Dinner Peak into By.
The results of the Durbin-Watson test this time is:
The p-value is greater than 0.05, showing that autocorrelation is no longer present in the data. We now know that for the analyses we run next, in order to account for the time of day and day of week effects, we should include Day and Lunch Peak/Idle/Dinner Peak in By.
