The Tableau file “Flights_data.twbx” contains information on all U.S. airline flights from 2010 and 2011. Follow the steps below and create Tableau worksheets to answer each of the following questions.

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

INSTRUCTIONS:

1. Tableau 2019.4 MUST be used.
2. Assignment should be in. Twbx Extension.

 

3. Tableau workbook should have separate worksheets clearly labeled as “Question 1,” “Question 2,” … etc.

 

4. If a question asks to do more than simply create a visualization (i.e., answer a specific question in text), please do so in the title of the worksheet or in a caption/annotation.

Datasets

 

• twbx
• Real Estate.xlsx
• Real Estate – Supplement.xlsx
• Data Set - ca-census.xlsx
• Data Set - lobbying-california.xlsx
• twbx

Case 1:

The Tableau file “Flights_data.twbx” contains information on all U.S. airline flights from 2010 and 2011.

Follow the steps below and create Tableau worksheets to answer each of the following questions.

1. A quick visualization of flights over time shows a drop of more than 300,000 flights from 2010 to 2011.

From your experience, you know that flight activity did not drop so significantly between 2010 and 2011. Why does this visualization seem to indicate otherwise? Justify your answer using an appropriate visualization.

2. Recreate the visualization shown in Figure 1 that depicts flight cancellations in orange. What were the reasons for the top four highest cancellation days? HINT: Google it!
3. For flights greater than 1000 miles flown, a user would like to know the average arrival delay into the destination airport per airline. Recreate the visualization in Figure 2. In that visualization, the delays are color coded as: 0 to 5 minutes (blue), 5+ to 10 minutes (orange), 10+ to 15 minutes (gray), and 15+ minutes (purple)

HINTS:

1. Start by creating a filter for flights where Distance (miles) is 1000 miles or more.
2. Create a calculated field to discretize Arrival Delay (Min). It will look something like:

IF AVG([Arrival Delay (Min)]) <= 5 THEN ‘Delay Less than 5 Mins’

ELSEIF AVG([Arrival Delay (Min)]) > 5 AND AVG([Arrival Delay (Min)]) <= 10 THEN ‘Delay

Between 5 and 10 Mins’

ELSEIF ???

ELSE ???

END

1. Use the resulting calculated field in your visualization and edit the colors in the legend

Case 2:

Open the MS Excel files “Real Estate.xlsx” and “Real Estate – Supplement.xlsx,” which contain fictitious real estate sales information. Follow the steps below and create Tableau worksheets (In the same Tableau workbook you used above) to answer each of the following questions.

4. Please recreate the visualization shown in Figure 3. DO NOT manually color each individual data point.

HINT: You can use exactly the same strategy as above to create a categorical variable associated with each color.

5. Please recreate the view shown in Figure 4. DO NOT manually color each individual data point. Please capture the equation details as an annotation. What do the colors indicate? On average, for every additional 100 square feet, how much longer can a seller expect their home to remain on the market?

HINTS:

1. You can use the ID field for your marker details.
2. Analysis>Trend Lines>Show Trend Lines
3. Hovering over the trend line will give you the equation details
4. Using the regression equation, create a new calculated field to assign colors
5. If you end up with one trend line for each color, right click a line and click Edit Trend Lines…. Then uncheck Allow a trend line per color.

Case 3:

Open the MS Excel files “Data Set - ca-census.xlsx” and “Data Set - lobbying-california.xlsx,” which contain information about the origin of money used for lobbying purposes in California. For both questions, include only those cities whose location coordinates are known and whose populations is at least 10,000. Use the “Name-use,” “Session-Amount,” and “Pop100” fields as you create these visualizations. Follow the steps below and create Tableau worksheets (In the same Tableau workbook you used above) to answer each of the following questions.

 

6. Please recreate the view shown in Figure 5. Use an annotation to identify the city that gave the most per capita and the amount given. The annotation should be “automatic” (i.e., tied directly to the data and not copied and pasted into a static text field)

 

HINTS:

 

6. Start by creating a left join on “Name-use” from the “All-data” steed from Data Set - lobbying-california and “Name-cleaned” from the “all_160_in_06.P1.csv” sheet from Data Set - ca-census.
7. You can also apply a filter on the Data Source tab to select only the locations with populations (Pop100) of at least 10,000. This way the filter will apply to both visualizations.

 

1. The question asks for money given (“Session-Amount”) per capita. This implies you need to create a Session-Amount per capita calculated field.
2. You have latitude and longitude, those will go in your rows and columns respectively. Under the All-data table heading, you need to change the geographic role of “Name-use” to City. It can then be used as detail on your map.

 

7. Please recreate the view shown in Figure 6. For cities that gave at least 1 cent per capita, which city gave the least per capita and how much was given?

 

Case 4:

 

Open the packaged Tableau file “Telecommunications.twbx,” which contains information about cellular telephone calls.

 

8. Recreate the exact view shown in Figure 7, which shows the hourly detail of the “Dropped Call Percentage by Tower” (where the “Dropped Call Percentage” is the percentage of “Attempted” calls that are “Dropped”)?

 

HINTS:

 

1. To format the Hours across the top, click the down arrow in the HOUR(Date String) pill and select Format and change the Dates format to 12-hour.
2. The rows have been manually sorted
3. Dropped Call Percentages of zero have been filtered out
4. The color scheme is “Custom Diverging” with 5 steps.

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