Data Visualization and Communication with Tableau Quiz Answers

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Week 01: Data Visualization and Communication with Tableau Quiz Answers

Quiz 1: Week 1 Quiz

Q1. According to the data analytics company Gartner, the majority of companies reported the reason their big data projects failed is due to:

• organizational failures only
• mostly organizational failures, but also some technical failures
• mostly technical failures, but also some organizational failures
• equal parts organizational and technical failures

Q2. True/False: Everybody in the analytics world agrees that your job as a data analyst is to ask and answer the one, true, right question.

• True
• False

Q3. In this course, we used the S.M.A.R.T acronym to refer to goals that are:

• Strategic, Measureable, Attainable, Relevant, and Time-bound
• Specific, Measureable, Active, Realistic, and Time-bound
• Specific, Measureable, Attainable, Relevant, and Time-bound
• Specific, Measureable, Attainable, Realistic, and Time-bound

Q4. Which of the following steps of the analytics project life cycle should you complete first?

• Making a dashboard
• Defining a SMART goal
• Analyzing data
• Making a data analysis plan

Q5. True/False: Including the business metric that you will use to assess success in your business analytics project will make your project goal sufficiently specific to ensure success.

• True
• False

Q6. Which of the SMART criteria are NOT met by this data analytics project goal (pay close attention to whether the options are words the SMART acronym stands for)?

The goal of this project is to, within 45 days, use the inventory, production, and customer service databases from national manufacturing plants to reduce the number of polo shirts returned for manufacturing defects.

• Attainable
• Specific
• Time-bound
• Measurable
• Reasonable

Q7. True/False: Stakeholders are those individuals who are influenced by the outcome of your project or who might have a strong interest in it.

• True
• False

Q8. Elicitation sessions occur:

• before you have accepted an analysis job
• after you have accepted an analysis job, and should conclude mid-way through a data analysis
• after you have accepted an analysis job, and should conclude before you begin a data analysis
• after you have accepted an analysis job, and should continue throughout the life cycle of a data analysis project

Q9. You are an analyst at a bank. Your bank ran a short-term experiment in which they provided credit cards to a wider range of applicants then normal, including applicants whose applications would typically be rejected due to their troubled financial profiles. Your job is to model the data from this experiment to create a decision-making algorithm that will help the bank decide how to respond to future credit card applications more profitably.

The following parties are NOT likely to be key stakeholder(s) in your project (select all that apply):

• the bank’s creditors
• the bank, itself
• the bank’s shareholders
• Individuals who will apply for credit cards in the future.
• Individuals who applied for credit cards in the past, but were denied

Q10. At the beginning of any data analysis project, you should meet with your key stakeholders to understand:

• what problem the business is experiencing that stakeholders hope to solve.
• whether all the stakeholders agree about what business problem needs to be solved.
• what types of effects or factors you should be considering in your data analysis.
• what kinds of analytic tools and results the company culture will be open to.
• all of the above.

Q11. According to Doug Laney, VP of Information Innovation and Strategy at Gartner, why don’t more companies move higher on the analytics continuum? (check all that apply)

• Inertia (comfort with the way businesses did things in the past)
• The financial burden of implementing more sophisticated data analytics is too high
• Decision-makers view analytic analyses as “black boxes” they don’t understand
• Implementing analytic solution higher up on the analytics continuum would require hiring more skilled employees than the company could accommodate

Q12. True/False: When you are designing your business analysis plan, you should treat your business metric as the independent variable in your data analysis.

• True
• False

Q13. SPAPs should be altered after you begin a data analysis.

• True
• False

Q14. How many layers should there be in the “independent variables” section of your pyramid?

• 2
• 3
• 4
• As many layers and sub-layers as needed to cover all the issues you want to be sure you document or address

Q15. The best charts for assessing the categories and subcategories listed in the intermediate layers of your SPAP include (select all that apply):

• Bar charts
• Scatter plots
• Pie charts
• Line charts

Week 02: Data Visualization and Communication with Tableau Quiz Answers

Q1. Use the Dognition_aggregated_by_DogID data set for the quiz questions. Note that we use comma (,) to separate groups of thousands in numbers.

How many unique human user IDs are there in the Dognition_aggregated_by_DogID data set?

• 14,433
• 16,261
• 17,985
• 17,986
• None of the above

Q2. What feature is common to all the rows that have a value of 37 in the “State” field of the Dognition_aggregated_by_DogID data set? Check all that apply.

• They are all from Estonia
• They are all “charmers”
• They are all golden retrievers
• They all completed 37 tests
• They all weigh 37 pounds

Q3. What property is common to almost all the data points that had “Sign In Counts” of greater than 175 in the Dognition_aggregated_by_DogID data set?

• They all have an Einstein profile
• They are all from the city Saint Jean de Monts
• They are all from Estonia
• They all have Dog ID fd51b784-7144-11e5-ba71-058fbc01cf0b
• They are all Shih Tzus that weigh 190 lbs

Q4. The Personality Dimension that has the highest average number of completed tests in the Dognition_aggregated_by_DogID data set is clearly:

• The Expert Dimension
• The Socialite Dimension
• The Renaissance Dimension
• The Protodog Dimension
• None of them. All of the personality dimensions have very similar completion rates.

Q5. In the Dognition_aggregated_by_DogID data set, what is consistent about the relationship between breeding group and number of tests completed, regardless of whether you aggregate the variable representing the number of tests completed by the median or the average of the breeding group?

• Herding dogs finish the greatest number of tests
• Sporting dogs finish the greatest number of tests
• Toy dogs complete the least number of tests
• Hound dogs complete the least number of tests
• None of the above

Q6. In the Dognition_aggregated_by_DogID data set, what personality type has the strongest representation (greatest number of records) in the sporting breed group?

• Socialite
• Charmer
• Protodog
• Expert
• Einstein

Q7. In the Dognition_aggregated_by_DogID data set, which of the following describes the median number of tests dogs of different breed types complete?

• Cross-Breed dogs complete a median of 8 tests, while all the other breed types complete a median of 7 tests
• All breed types complete a median of 5 tests
• All breed types complete a median of 7 tests
• Pure-Breed dogs complete a median of 8 tests, while all the other breed types complete a median of 7 tests
• Popular-Hybrid dogs complete a median of 8 tests, while all the other breed types complete a median of 7 tests

Q8. In the Dognition_aggregated_by_DogID data set, how do the average number of tests dogs complete compare for fixed vs. not fixed dogs across different breed types? Click all that apply

• The greatest difference between the average number of tests completed by fixed vs. non-fixed dogs occurs in the Mixed Breed/ I Don’t Know breed category
• Fixed dogs complete more tests than non-fixed dogs in all breed types
• Fixed dogs complete more tests than non-fixed dogs in the Cross-Breed and Mixed Breed/ I Don’t Know breed categories, but not in the other breed categories
• Fixed dogs complete less tests than non-fixed dogs in all breed types
• Fixed dogs complete less tests than non-fixed dogs in the Cross-Breed and Mixed Breed/ I Don’t Know breed categories, but not in the other breed categories

Q9. In the Dognition_aggregated_by_DogID data set, which of the following are true about the average number of tests dogs complete when comparing DNA vs. not DNA-tested dogs who were fixed vs. not fixed across different breed types? Click all that apply.

• The Cross-Breed dogs that were DNA tested but NOT fixed were mostly Golden Doodles
• DNA-tested dogs completed more tests than dogs that were NOT DNA tested in all categories except for the category of Popular Hybrids who were fixed
• There was only one dog in the Popular Hybrid breed category who was DNA tested but not fixed
• DNA-tested dogs completed less tests than dogs that were NOT DNA tested in all categories except for the category of Popular Hybrids who were fixed
• The Cross-Breed dogs that were DNA tested but NOT fixed were mostly Labrador Retriever-Golden Retriever Mixes

Q10. In the Dognition_aggregated_by_DogID data set, when you make a filled map that displays the number of unique Dog IDs in each country, there is country in Africa that has a deep color, suggesting it has a lot of users. When you hover over that country, what Country is displayed on the tool tip?

• AR
• NO
• SA
• ZA
• N/A

Q11. In the Dognition_aggregated_by_DogID data set, which state within the United States has the most Dognition customers?

• Florida, with Texas having the second greatest number of customers
• Texas, with California having the second greatest number of customers
• North Carolina, with New York having the second greatest number of customers
• California, with New York having the second greatest number of customers
• North Carolina, with California having the second greatest number of customers

Q12. In the Dognition_aggregated_by_DogID data set, dogs in which of the following states did customers complete a median number of tests that was greater than 13? Check all that apply.

• Maine (ME)
• North Carolina (NC)
• North Dakota (ND)
• South Dakota (SD)
• Wyoming (WY)

Q13. Which of the following is true?

• Average and median aggregations are equally sensitive to outliers
• Average aggregations are more sensitive to extreme values than median aggregations
• Median aggregations are more sensitive to extreme values than average aggregations
• Neither average nor median aggregations are sensitive to outliers

Q14. In the Dognition_aggregated_by_DogID data set, when looking at only dogs who completed 19 or less tests, which of the following is true about the relationship between inter-test intervals (ITIs) and number of tests completed? Click all that apply.

• There was a non-significant negative (p > .05) correlation between average ITIs and number of tests completed
• There was a significant positive (p < .05) correlation between median ITIs and number of tests completed
• There was a significant negative (p < .05) correlation between median ITIs and number of tests completed
• There was a non-significant positive (p > .05) correlation between average ITIs and number of tests completed
• There was a significant positive (p < .05) correlation between average ITIs and number of tests completed

Q15. In the Dognition_aggregated_by_DogID data set, when looking at only dogs who completed 7 or less tests, which of the following is true about the relationship between inter-test intervals (ITIs) and number of tests completed? Click all that apply

• There was a non-significant negative (p > .05) correlation between average ITIs and number of tests completed
• There was a significant positive (p < .05) correlation between median ITIs and number of tests completed
• There was a significant positive (p < .05) correlation between average ITIs and number of tests completed
• There was a significant negative (p < .05) correlation between median ITIs and number of tests completed
• There was a non-significant positive (p > .05) correlation between average ITIs and number of tests completed

Week 03: Data Visualization and Communication with Tableau Quiz Answers

Q1. Use the dognition_data_no_aggregation data set provided in this course for this quiz.

In the dognition_data_no_aggregation data set, the greatest drop offs of test-takers occur after a subcategory of tests is completed rather than while the subcategories of tests are still in progress.

• True
• False

Q2. In the dognition_data_no_aggregation data set, after which game is the drop-off of completed tests the greatest?

• Inferential Reasoning Game
• Cunning Game
• Yawn Game
• Memory vs. Pointing Game
• Eye Contact Game

Q3. If you remove all the entries associated with Shih Tzu dogs that weigh 190 pounds from the dognition_data_no_aggregation data set, how many different sequences of tests were administered to customers in the data that are left?

• 1
• 2
• 3
• at least 4
• at least 15

Q4. During which day of the week do customers play Dognition games the most?

• Wednesday
• Thursday
• Friday
• Saturday
• Sunday

Q5. During which month in the data set were the most tests completed?

• August 2013
• March 2014
• October 2014
• June 2015

Q6. After adjusting the time stamps of the tests completed by United States users provided in the “Created At” field of the dognition_data_no_aggregation data set for time zone differences, during which hour of the day do Dognition customers play the most amount of games?

• 7 AM
• noon
• 3 PM
• 7 PM
• 10 PM

Q7. In the dognition_data_no_aggregation data set, approximately what percentage of users who begin taking the Dognition Assessment complete 20 tests?

• 6%
• 8%
• 15%
• 23%
• 25%

Q8. In the dognition_data_no_aggregation data set, what percentage of users who begin taking the Dognition Assessment using a “Free Start” promotion complete 20 tests?

• 1%
• 6%
• 17%
• 22%
• 29%

Q9. Using the dognition_data_no_aggregation data set, if you make a table using the “Dog ID” and “Test Name” variables on the rows shelf, and Created At on the Text property of the Marks card, which of the following might be a value you would see in the column farthest to the right, if the “Created At” pill was blue and read SECOND(Created At)?

• 2/8/2013 1:39:00 AM
• 0
• 39
• 1:39:00 AM

Q10. If you were writing a calculation to rank each test a dog completed by its time stamp in the dognition_data_no_aggregation data set, what would be missing from the following version of your rank table calculation: RANK(([Created At]),’asc’)

• an aggregation before [Created At], the most appropriate of which is MEDIAN
• an aggregation after [Created At], the most appropriate of which is MEDIAN
• an aggregation before [Created At], the most appropriate of which is ATTR
• an aggregation after [Created At], the most appropriate of which is ATTR
• there is nothing missing

Q11. You are writing a calculation to rank each test a dog completed by its time stamp in the dognition_data_no_aggregation data set. You’ve configured your table calculation so that “Dog ID” and “Test Name” are in the Partitioning field and “Second Created” is in the Addressing field. The resulting rank will:

• Provide one number for every test within a subcategory
• List “1” for the first test of every Dog ID
• Provide one correctly ranked number for every test within a single Dog ID
• Provide one incorrectly ranked number for every test within a single Dog ID

Q12. You are writing a calculation to include a column in a table that ranks each test a human user completed by its time stamp in the dognition_data_no_aggregation data set. You put this column directly to the left of the column you made to rank each test a dog completed by its time stamp. The following field(s) will go in the Partitioning field in the calculation configuration page in your new calculation meant to rank each test a human user completed: (check all that apply)

Created At

• Dog ID
• Test Name
• User ID
• None of the above

Week 04: Data Visualization and Communication with Tableau Quiz Answers

Q1. The hourglass model for structuring effective business presentations suggests you should:

• begin your presentation with motivating context and end with your business recommendation
• begin and end your presentation with motivating context
• begin your presentation with your agenda and end with a summary of what you covered
• begin your presentation with your agenda and end with a story

Q2. Beginning your business presentation in the middle of the plot of a motivational story can sometimes be an effective way to lead into your business recommendation.

• True
• False

Q3. The storyboarding process includes:

• asking for feedback
• determining the precise order in which the scenes will be organized
• choosing the best visualizations to communicate the information of each scene
• narrowing in on the minimum number of scenes necessary to convey your data story
• all of the above

Q4. According to
the psychology literature, if the business recommendation you are going to make
in a business presentation is likely to be controversial, you should order the
stem of your presentation hourglass so that the:

• least controversial point is presented first.
• least complicated story point is presented first.
• strongest story point is presented first.
• most emotional story point is presented first.

Q5. As a data analyst, you can avoid the logical fallacy of overgeneralization by:

• insisting that you are given a very large data set to analyze.
• selecting a small, unbiased sample.
• ensuring the data you work with is a selected subset with a strong data story.
• none of the above

Q6. To test whether a certain advertising campaign would work, an analytics team sorts
their customer list from lowest to highest customer ID number, and then sends
their advertisement to the first 1000 customers on the list. The rest of the customers did not receive any
advertisements that week. When analyzing
the results of the campaign one week later, the analytics team realized that there
was a previously unknown pattern in the customer ID numbers: the lower the
customer number, the longer the person had been a customer. Thus,
the customers who received the advertisement were the individuals who had been
customers with the company the longest.
The analytics team decided the test was invalid and needed to be
repeated. The reason for their decision
was that analyzing the results in their current form would result in the
following logical fallacy (or fallacies):

• Over-generalization
• Inferring causation from correlation
• Lack of controls
• All of the above
• None of the above

Q7. When two variables are correlated, one variable does not cause the other variable.

• True
• False

Q8. When tests can’t be
run, which of the following can data analysts do to assess the degree of
confidence one should have in the nature of a correlation between two
variables? Choose all that apply.

• Identify different but complementary ways to use the same data set to assess the causal relationship about which you are hypothesizing.
• Infer that if the observed effect is extremely large or obvious, it is likely real.
• Attempt to replicate the effect by examining whether the correlation on which you are basing your business recommendation exists in other data sets or contexts.
• Assess whether there are additional variables that can explain the relationship.

Q9. Which of these charts would be the best way to display how Smartphone sales have changed over time?

• Neith Chart A nor Chart B is effective
• Chart B
• Chart A
• Chart A and Chart B are equally effective

Q10. When you want to represent very detailed
and nuanced information about continuous variables, given humans’ ability to
perceive relative differences along different kinds of visual attributes, which
of the following attributes should you exploit in your visualizations? Choose the best 2 options.

• Area
• Color
• Length
• Volume
• Position

Q11. If you are in a situation
where you MUST use colorbars to represent detailed information about a continuous
variable, you should:

• use colors that are very bright so that they can easily be detected.
• use a gray scale that goes from black to white.
• use a colorbar that color-blind people can perceive.
• use colorbars that only have gradations from one color to a second color so that the audience isn’t distracted by excess color.

Q12. Visualizations for persuasion should: (Choose all that apply)

• show as much data as possible.
• direct your audience’s eyes to the precise points of the data that support your argument.
• show selected pieces of data.
• show the visualizations in an order that helps your audience evaluate the options clearly.

Q13. Which of the following
reflect(s) the principles of maximizing the data-ink ratio? Choose all that apply.

• Reducing the amount of text on the slide
• Making the borders of bars in a graph the same as the slide background
• Including a bar at the bottom of the slide to indicate how far along you are in your presentation
• Making the scales and labels of the graph easy to read

Q14. It’s a good idea to apply
the rule of thirds to: (Choose all that apply)

• Transition slides
• Slides illustrating stories
• Soft break slides
• Slides meant to catch your audience’s attention
• Slides containing data

Q15. Effective presentation techniques include:

• keeping a physically open posture by keeping your arms away from the front of your body
• facing your audience and looking at different people in the room
• being natural in your movements
• refraining from looking down or reading your slides.
• all of the above

Q16. A hospital was having problems with the amount of time
employees with direct care responsibilities were absent from work. Due to the
high levels of absenteeism, patient satisfaction
was declining, 20% of patient-related
work was not getting done, and 47% of non-patient work was not getting
done. At the advice of a consulting
company, the hospital implemented a positive incentive system that would allow
all employees to convert up to 24 hours of unused sick time into additional pay or more vacation days in
order to reduce absenteeism. After 6
months of implementing the program, the hospital analysts calculated that
absentee rates declined an average of 11.5 hours per employee, and concluded that
the program was successful in this company.
Did the hospital analysts commit any logical fallacies when arriving at
their conclusion, and if so, which fallacy (or fallacies)?

• Lack of controls
• Overgeneralization
• Inferring causation from correlation
• None of the above
• All of the above

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