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.

The sales consistently stay in the 40k range, but in week 23, there is a dip in sales down to 22,824.

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).

The new graph shows the sales averge to be in the 20k range opposed to the 40k range as the previous graph.

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.

As seen in the graph above, the lower the sales are, the lower the temperature is, and as the sales increase so does the temperature. For example, in week 30, the average temperature was 30 and the sales were at their highest point at 26,774.

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?

In the picture above, it seems that there is no distinct pattern in correlation for money spent on TV ads and an increase in tv sales. Where the tv ads mark is 0, there is a wide range of low-high tv’s sold.

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.

The radio sales have a higher impact than the TV sales. The radio ads helped the sales of TVs also.

In a separate Tableau sheet

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

Sales and fuel volume are positively correlated, and as the temperature increases so do the sales.

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

This graph shows that when there is a holiday people are more likely to go to a store.

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.

After viewing all of the data, I think that temperature most affects the stores sales. Fuel volume also affected the sales, but more people were likely to go inside and buy in order to increase sales as the temperature increase.