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 graph we can see that sales, from the start of the year until week 22, are following an uptrend. At week 22 sales dropped significantly and have stayed low until week 25 in which increased drastically. Since week 26 until week 37 sales were continously decreasing. During the period between week 38 and week 52, sales have followed the same behavior moving up and down within the range of ~39,000 and ~46,000.

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).

Since now sales are being measured in average, they are lower. Nonetheless, the trend followed is the same as in Task 1A, in other words, increasing by the beginning of the year, dropping significantly by the middle and downtrend towards the end.

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.

Based on the graph we can say that the combination of temperature and sales are positively correlated, in other words, the higher the temperature the higher the sales and the lower the sales the lower the temperature.

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 this graph we can observe that it is a great number of sales when the targeted audience reached is 0. It is also observed that the higher the number of TV ads the higher the sales. Although there is an upper limit to the amount of TV ads because after such the sales start dimishing, as this graph illustrate; in other word,s this will have a negative effect in sales. The inflection point seems to be ~90 tv ads. It is hard to tell the amount that should be invested in TV ads mainly because the data is not given in dollars but instead it is measured in GRP units.

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.

In a separate Tableau sheet

The trend that follows is similar, there is a point of decreasing returns when it comes to sales. According to the graph spending more on radio and tv ads will lead to increasing amount of sales. It is also remarkable to emntion that radio ads seem to be more successful since we can see a majority of sales above 25k even when there is no tv ad occurring.

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

There is still a similar trend between fuel and sales, sales goes up as fuel volume increases. There are two things that can also be observed: fluctuation among peaks is still visible and after 66K there is not so much impact.

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

As seen in the graph, the higher the temperature the higher the number of sales. Fluctuations are still visible throughout sales but it is clear that when temperature increases sales also increase. ##### 2E) Overlay Holiday using the Label scale. Include a snapshot here. Explain the new combined behavior and the impact of Holiday.

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.

The treemap as well as the scatter plot, they both show a positive relationship between sales and the other variables. The higher the sales the higher the temperature and the higher the fuel volume. The number of holidays during hotter temperature is greater and we could say that a combination of these both variables could also affect positively sales. It is important to mention that fuel volume has a negative impact on sales after 60k ls.