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Chapter 10: Reporting Results

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Chapter 10: Reporting Results

16 problems · 0 figures

10-1 §10.1 source ↗

In a technical data science report, what type of communication is most suitable for executives?
  1. a.
    Highly technical and detailed
  2. b.
    Focused on practical application
  3. c.
    Concise with a focus on summaries and outcomes
  4. d.
    Broad and nontechnical for general understanding

Answer

C. Concise with a focus on summaries and outcomes

10-2 §10.1 source ↗

What should a technical report for nonspecialists avoid?
  1. a.
    Complex statistical data
  2. b.
    Technical jargon
  3. c.
    Visual aids like graphs and charts
  4. d.
    Descriptions of practical applications

Answer

B. Technical jargon

10-3 §10.1 source ↗

Which is a key consideration when writing technical data science reports?
  1. a.
    Using complex language to demonstrate expertise
  2. b.
    Connecting with diverse audiences through tailored communication
  3. c.
    Focusing only on the most advanced techniques
  4. d.
    Avoiding visual aids to prevent confusion

Answer

B. Connecting with diverse audiences through tailored communication

10-4 §10.1 source ↗

For technicians, a data science report should primarily focus on:
  1. a.
    Theoretical knowledge
  2. b.
    Practical application and procedures
  3. c.
    Advanced research and new findings
  4. d.
    Business implications and strategies

Answer

B. Practical application and procedures

10-5 §10.1 source ↗

What is the purpose of a layered approach in a data science report?
  1. a.
    To provide different levels of detail for various audiences
  2. b.
    To simplify the report writing process
  3. c.
    To focus solely on the technical aspects
  4. d.
    To avoid including any technical language

Answer

A. To provide different levels of detail for various audiences

10-6 §10.1 source ↗

What is the primary role of visual aids like graphs and tables in a data science report?
  1. a.
    To replace written content
  2. b.
    To add aesthetic appeal to the report
  3. c.
    To clarify and accentuate significant data insights
  4. d.
    To demonstrate the author's technical skills

Answer

C. To clarify and accentuate significant data insights

10-7 §10.1 source ↗

In a technical report, how should graphs and tables be presented to the reader?
  1. a.
    In a complex and detailed manner
  2. b.
    Randomly placed throughout the report
  3. c.
    Without any accompanying explanations
  4. d.
    With clear labels, titles, and straightforward explanations

Answer

D. With clear labels, titles, and straightforward explanations

10-8 §10.2 source ↗

What is the primary purpose of documenting assumptions in a data science report?
  1. a.
    To prove the assumptions are correct
  2. b.
    To steer the decision-making process
  3. c.
    To increase the complexity of the report
  4. d.
    To show the expertise of the report writer

Answer

B. To steer the decision-making process

10-9 §10.2 source ↗

What is a key characteristic of a well-stated assumption in a technical report?
  1. a.
    It is based on personal opinion.
  2. b.
    It is specific and verifiable.
  3. c.
    It is vague and general.
  4. d.
    It is always proven to be true.

Answer

B. It is specific and verifiable.

10-10 §10.2 source ↗

In the context of measuring house prices, what does an R-squared value of 0.75 indicate?
  1. a.
    The model predicts housing prices with 75% accuracy.
  2. b.
    Seventy-five percent of the variance in housing prices is explained by the model.
  3. c.
    The model is 75% reliable.
  4. d.
    The model will increase housing prices by 75%.

Answer

B. Seventy-five percent of the variance in housing prices is explained by the model.

10-11 §10.2 source ↗

Why is it important to consider additional metrics beyond accuracy in a classification model?
  1. a.
    To comply with industry standards
  2. b.
    To understand the model's performance more deeply
  3. c.
    To make the report longer
  4. d.
    To use up more computational resources

Answer

B. To understand the model's performance more deeply

10-12 §10.2 source ↗

What is the purpose of conducting a sensitivity analysis in a data science project?
  1. a.
    To test the robustness of results against input variability
  2. b.
    To reduce the number of input variables
  3. c.
    To increase the complexity of the model
  4. d.
    To make the report more appealing

Answer

A. To test the robustness of results against input variability

10-13 §10.3 source ↗

What is the primary purpose of an executive summary in a data science report?
  1. a.
    To present complex technical analyses in a detailed manner
  2. b.
    To provide a comprehensive background of the data scientists
  3. c.
    To summarize complex analyses for an audience of varied backgrounds
  4. d.
    To list all the data sources and methodologies in detail

Answer

C. To summarize complex analyses for an audience of varied backgrounds

10-14 §10.3 source ↗

14. In a data science report, what should the executive summary ideally begin with?
  1. a.
    A detailed description of the data
  2. b.
    An introduction to the business problem or research question
  3. c.
    Advanced statistical formulas
  4. d.
    The names and credentials of the data scientists

Answer

B. An introduction to the business problem or research question

10-15 §10.3 source ↗

What aspect of the methodologies should be highlighted in the executive summary of a technical data science report?
  1. a.
    The complexity of the methodologies
  2. b.
    The novelty or relevance of the approach
  3. c.
    Every step in the methodological process
  4. d.
    The technical language used in the methodologies

Answer

B. The novelty or relevance of the approach

10-16 §10.3 source ↗

How should the executive summary in a data science report conclude?
  1. a.
    With a persuasive call to action
  2. b.
    By summarizing the data sources used
  3. c.
    With a detailed technical explanation
  4. d.
    By introducing new topics

Answer

A. With a persuasive call to action

End of the book.

Problems and answers are adapted from Principles of Data Science by Shaun V. Ault, Soohyun Nam Liao and Larry Musolino, OpenStax, and are used under the CC BY-NC-SA 4.0 licence. Access for free at https://openstax.org/books/principles-data-science/pages/1-introduction. Illustrations: Copyright Rice University, OpenStax, under CC BY-NC-SA 4.0 license. This adaptation is released under the same licence, is non-commercial, and is neither affiliated with nor endorsed by OpenStax or Rice University.