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 data shows a line graph with the sum of sales for each week over about 2 years. The data was increasing for the first 21 weeks and then decreased dramatically around weeks 22-24. The sales then increased again around weeks 25-26, but then started to gradually decrease for the rest of the year. We are missing a data point for weeks 23-25.

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 data shows a line graph with the average sales for each week over about 2 years. By averaging the two week points we are ignoring the weeks with missing data. There is now only one sales amount per week, which corrects the problem faced in task 1A.

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.

With the addition of temp, we can see a slight relationship between temperature and sales. The warmer it is, the better the sales are and vice versa. There are higher sales in the summer when it is hotter.

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?

The scatter plot correlates the sales with the amount of advertising spent on tv during that period. While it can possibly be correlated that if you spend around 90K that you will get varying sales, the graph shows that spending no money will result in sales. Based on the chart, it can be said that there is little or no correlation between spending on advertising and sales. The upper limit should be around 90K or the amount of investment does not match how much money is made from sales.

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.

By adding overlay radio, there is a greater correlation between the amount spend on radio ad and sales. The greater amount of money that is spent on Radio ad, the greater potential for sales is shown.

In a separate Tableau sheet

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

The graph shows a positive correlation between fuel volume and sales.

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

When the temperature is newar 30 degrees, customers tend to spend more than when it is colder, making sales and fuel volume higher. However, there is not enough analysis to confirm this. ##### 2E) Overlay Holiday using the Label scale. Include a snapshot here. Explain the new combined behavior and the impact of Holiday.
On holidays, sales tend to be higher than days that are not holidays.

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.

I think holidays and temperature affect sales the most, although holidays have the greatest affecct on sales. There is a strong positive correlation between high temperature and days that are holidays and the values of sales being high. TV and radio also affect sales but they do not increase sales nearly as much as holidays and temperature.