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.

  • Sales usually stays relatively steady, between about 40,000 and 54,000. However, around week 22 sales decrease steeply, hitting a low point of about 23,000 at week 23, and do not recover until about week 26.
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).

Instead of showing the total sales for the week, this graph shows the average amount of sales per week. This graph does still follow the same pattern as the graph in 1a, which is to be expected. For example, both graphs show a steep decrease in sales at about week 22 to week 26. However, this graph also shows behavior that was not as clear in 1a, for example, between about weeks 33 to 37 the average sales decreased steadily.

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.

There is a direct relationship between Sales and Temperature, so that when Temperature increases, Sales increase too. Basically this graph tells us that Sales depend on good weather. This means that the business is more profitable during spring and summer months, when the weather is warmer.

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?

By looking at the graph, we can tell that most sales are happening with less than 100 being spent on TV ads. Even when the amount being spent on TV is 0, there is still a significant amount of sales being made. I think that the upper limit amount that should be invested in TV ads is 100 because very few sales are made when the amount spend on TV ads is higher than 100.

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.

  • Radio ads have a high impact as Sales increase. For a value of TV and Radio ads of zero, there is a wide range of Sales values. However, when Sales are high, Radio ads tend to be high as well. On the other hand, when TV ads are large, so are Radio ads.

In a separate Tableau sheet

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

As long as the station sells volume of fuel then there is an inrease in the store sales as well. There is a positive correlation between the fuel volume and the sales. which means that as the value of fuel volume increases, the value of sales also increases.

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

We can now see the impact of the temperature on fuel volume and sales. It seems that when the temperatures are higher, both fuel volume and sales have higher values, and that when temperatures are lower, sales and fuel volume have lower values

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

Sales and Fuel Volume seem to be positively correlated with the Holiday variable. The higher the Sales and Fuel Volume are, the more likely that there is a Holiday. Temperature also tendS to be higher for the holidays as well, and both are tied to increasing Sales.

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.

Temperature and Holiday affect sales the most. There is a strong positive correlation between high Temperature and there being a Holiday and the value of Sales being high. The highest amount of Sales will occur during a Holiday in the summer months, when the weather is warmer. TV and Radio ads did help increase sales somewhat, but their influence was not nearly as strong.