About

In this section, we will be using Tableau to learn concepts on data outliers, seasonality effect, and the relationships and impacts. There is no R coding in this lab session.

Setup

This worksheet will be used to capture your images from Tableau and to share your observations. Example of capturing and including an image is included at the end of this sheet for your reference. You will need to log onto Tableau and Connect/Import the file EuroStore.xls found in the ‘bsad_lab10’ folder.

Remember to always set your working directory to the source file location. Go to ‘Session’, scroll down to ‘Set Working Directory’, and click ‘To Source File Location’. Read carefully the below and follow the instructions to complete the tasks and answer any questions. Submit your work to RPubs as detailed in previous notes.

Note

For your assignment you may be using different data sets than what is included here. Always read carefully the instructions on Sakai. Tasks/questions to be completed/answered are highlighted in larger bolded fonts and numbered according to their particular placement in the task section.

Task 1: Data Outliers and Seasonality Effect

First get familiar with the data and what each columns represent. A description of the data is provided in a seperate sheet called ‘Desc’ in the same Excel file. Refer to Lab05 for early exercise using Tableau.

In a new Tableau sheet

1A) Plot Sales (Rows) versus Week (Columns). Include a snapshot here. Analyse the data source and explain in clear words the behavior you observe.

In this plot the behavior observed is that between weeks 23-25 there is a huge drop in sales, before it picks up again. This drop could be explained though a seasonal trend, which would require more years of data to confirm, or it could be something else the company did which caused the sales to have such a steep drop, this would also require more data to confirm.

1B) Switch from SUM(Sales) to Average AVG(Sales). Change the Sales scale to be more reflective of the data. Include a snapshot here. Explain the new behavior relative to 1A).

When looking at 1B the drop between week 23 and 25 is not nearly as big. After week 30 there is also a significant decrese in the average sales.

1C) Add Temp to the Color scale found in Marks. Change SUM(Temp) to AVG(Temp). Edit the color legend to be more reflective of hot and cold temperatures. Include a snapshot here. Explain the combined behavior of sales and temperature.

The combination of sales and emperature shows that when it is warmer outside around week 30the average sales is quite high, while when it is colder outside, during weeks 1 and 52, the average sales are much lower.

Task 2: Relationships and Impacts

In a seperate Tableau sheet

2A) Plot Sales (Rows) versus TV (Columns). Switch both measures from SUM() to Dimension. The plot should look more like a scatter plot. Include a snapshot here. Explain the behavior of Sales versus TV. How much you think is the upper limit amount that should be invested in TV ads?

There is a small correlation between sales and TV. As TV reach gets higher there are point were the sales do not go below a certain point. The maximum limit of TV spending is 28K as that is the most sales revenue. However, TV sending should also not go that high as it would cause the company profits to be very low.

2B) Overlay Radio to the previous plot using the Size. scale found in Marks. Include a snapshot here. Explain how the additional Radio ads to Tv ads is impacting Sales.

Adding TV to this plot shows a greater realtionship between sales and ad spending. As more is spent on TV and radio ads sales are typycally higher.

In a separate Tableau sheet

2C) Plot Sales versus Fuel Volume. Explain behavior.

There seems to be a visible correlation between average fuel volume sold and the sales. This could be explained because larger volumes of fuel sold cost more and therefore bring up the sales.

2D) Overlay Temperature using the Color scale. Follow 1C) for temperature settings. Explain the new combined behavior and the impact of temperature.

This graph shows that the during times with higer temeratures higer volumes of fuel are sold and sales is higher, while during lower temps it is the opposite.

2E) Overlay Holiday using the Label scale. Include a snapshot here. Explain the new combined behavior and the impact of Holiday.

If there is a holiday in a given week it seems as there are more sales in that week, however holidays and temparature are very realted because holidays always fall in the same time each year, and thus having both the holiday and temperature varables would not change predictions very much.

In a separate sheet

2F) Use a Tree Map to best show the combined effect of Sales, Fuel Volume, Temp, and Holiday. A sample view is shown below. Consider using the Quick Filter on Holiday and Temp to isolate and better view the impact of each. You can have more than one filter at a time. Include a snapshot here.
2G) Write a small paragraph summarizing your final conclusions on what you think most affect Sales and under what conditions.

From all the data that has been seen through tableau sales are most affected by average fuel volume and temerature. Thoughout all the graphs sales has been the highest with higher temperatures. It is also reasonable the higher feul vlmes sold would increase sales because more fuel costs more.