Reproducible Research Coursera Quiz Answers 2022 | All Weeks Assessment Answers [💯Correct Answer]

Hello Peers, Today we are going to share all week’s assessment and quiz answers of the Reproducible Research course launched by Coursera totally free of cost✅✅✅. This is a certification course for every interested student.

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Here, you will find Reproducible Research Exam Answers in Bold Color which are given below.

These answers are updated recently and are 100% correct✅ answers of all week, assessment, and final exam answers of Reproducible Research from Coursera Free Certification Course.

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About Reproducible Research Course

This course focuses on the concepts and methods underpinning reporting current data analytics in a reproducible manner. The concept of reproducible research refers to the practise of publishing data analyses and, more generally, scientific claims along with the underlying data and software code.

Course Apply Link – Reproducible Research

Reproducible Research Quiz Answers

Reproducible Research Quiz 1 Answers

Question 1
Suppose I conduct a study and publish my findings. Which of the following is an example of replication of my study?

  • I take my own data, analyze it again, and publish new findings.
  • An investigator at another institution conducts a study addressing a different scientific question and publishes her findings.
  • An investigator at another institution conducts a study addressing the same question, collects her own data, analyzes it separately from me, and publishes her own findings.
  • I give my data to an independent investigator at another institution, she analyzes the data and gets the same results as I originally obtained.


Question 2
Which of the following is a requirement for published data analysis to be reproducible?

  • The analysis is conducted on a variant of the Unix operating system.
  • The data analysis is conducted using R.
  • The full computer code for doing the data analysis is made publicly available.
  • The investigator’s final publication is made available free of charge.

Question 3
Which of the following is an example of a reproducible study?

  • The study’s original authors re-run their computer code on their analytic data and confirm publicly that the findings match those of the published results.
  • The study’s analytic data and computer code for the data analysis are publicly available. When the code is run on the analytic data, the findings are identical to the published results.
  • The study’s analytic data are publicly available, but the computer code is not.
  • The study’s analytic data and computer code are not publicly available, but the study was simple enough to be repeated by an independent investigator.

Question 4
Which of the following is a reason that a study might NOT be fully replicated?

  • The original study was very expensive and there is no money to repeat it in a different setting.
  • The original study was conducted by a well-known investigator.
  • The original study had null findings.
  • The original investigator does not want to make the analytic data available.

Question 5
Which of the following is a reason why publishing reproducible research is increasingly important?

  • New technologies are increasing the rate of data collection, creating datasets that are more complex and extremely high dimensional.
  • The statistical methods for most studies can be accurately described using plain language.
  • Computing power is limited today, making it difficult to apply sophisticated statistical methods.
  • Most studies today are small-scale and easily replicated.

Question 6
What is the role of processing code in the research pipeline?

  • It transforms the computational results into figures and tables.
  • It transforms the analytic data into computational results.
  • It conducts the statistical analysis of the primary outcome.
  • It transforms the measured data into analytic data.

Question 7
Which is the goal of literate statistical programming?

  • Ensure that data analysis documents are always exported in PDF format.
  • Separate figures and tables from other data analytic summaries.
  • Require that data analysis summary are always written in LaTeX.
  • Combine explanatory text and data analysis code in a single document.

Question 8
What does it mean to weave a literate statistical program?

  • Transform the literate program into a machine-readable code file.
  • Transform the literate program into a human-readable document.
  • Compress the literate program so that it takes up less space.
  • Transform a literate program from R to python.

Question 9
Which of the following is required to implement a literate programming system?

  • A Unix-based computer system.
  • A web server for publishing documents.
  • A documentation language like LaTeX.
  • A program that views PDF files.

Question 10
What is one way in which the knitr system differs from Sweave?

  • knitr allows for the use of markdown instead of LaTeX.
  • knitr lacks features like caching of code chunks.
  • knitr is written in python instead of R.
  • knitr was developed by Friedrich Leisch.

Reproducible Research Quiz 2 Answers

Question 1

Who created Markdown?

  • Robert Gentleman
  • Yihui Xie
  • John Gruber
  • Hadley Wickham

Answer Options:
John Gruber

Question 2

When writing a document in R Markdown, how do you denote the beginning of an R code chunk?

  • “`{r}
  • <rcode>
  • “`
  • <code>

Answer Options:
“`{r}

Question 3

When using knitr, how do you indicate the height and width of a plot created in a code chunk?

  • Set the ‘fig.height’ and ‘fig.width’ options for the code chunk
  • Set the ‘height’ and ‘width’ options for the code chunk
  • Set the ‘size’ and ‘scale’ options for the code chunk
  • Set the ‘dpi’ option for the code chunk

Answer Options:
Set the ‘fig.height’ and ‘fig.width’ options for the code chunk

Question 4

With some code chunks, we may not want the output generated by the chunk to be rendered into HTML but would prefer to print the output verbatim. How can we specify this preference for a given code chunk?

  • Set the option tidy = FALSE
  • Set the option message = FALSE
  • Set the option results = “asis”
  • Set the option highlight = TRUE

Answer Options:
Set the option results = “asis”

Question 5

When using knitr and R Markdown and producing output in HTML, why should you never edit the resulting HTML file?

  • Every time you knit() the R Markdown file, the HTML file will be overwritten
  • The HTML file is not a text file.
  • Editing the HTML file requires knowledge of a separate markup language.
  • The Markdown file generated by knitr is the appropriate file to edit.

Answer Options:
Every time you knit() the R Markdown file, the HTML file will be overwritten

More About This Course

This course focuses on the concepts and methods underpinning reporting current data analytics in a reproducible manner. The concept of reproducible research refers to the practise of publishing data analyses and, more generally, scientific claims along with the underlying data and software code. This enables other researchers to independently validate the findings and build upon them. The requirement for repeatability is increasing dramatically as data studies get more complicated, including larger datasets and more powerful computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. Because the data and code that were used to actually conduct the analysis are made public, an analysis that is reproducible also increases the use of the analysis for other people. This class will concentrate on literate statistical analysis tools, which enable one to publish data analyses in a single document, making it simple for others to carry out the same analysis and obtain the same results as the original researcher.

This course is part of multiple programs:

This course can be applied to multiple Specializations or Professional Certificates programmes. Completing this course will count towards your learning in any of the following programmes:

  • Data Science Specialization
  • Data Science: Foundations using R Specialization

WHAT YOU WILL LEARN

  • Organize data analysis to help make it more reproducible
  • Write up a reproducible data analysis using knitr
  • Determine the reproducibility of the analysis project
  • Publish reproducible web documents using Markdown

SKILLS YOU WILL GAIN

  • Knitr
  • Data Analysis
  • R Programming
  • Markup Language

Conclusion

Hopefully, this article will be useful for you to find all the Week, final assessment, and Peer Graded Assessment Answers of the Reproducible Research Quiz of Coursera and grab some premium knowledge with less effort. If this article really helped you in any way then make sure to share it with your friends on social media and let them also know about this amazing training. You can also check out our other course Answers. So, be with us guys we will share a lot more free courses and their exam/quiz solutions also, and follow our Techno-RJ Blog for more updates.

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