We strongly encourage you to take advantage of the fact that you are not
alone in the course and ask questions, answer each other, and discuss findings.
>> Next, you'll complete a peer assessment where you will do exploratory data
analysis modal selection and model evaluation.
The questions on this peer assessment will be similar to the ones you worked on in
the previous quiz.
You will complete this assessment by writing a data analysis report
with R Markdown, and submit your source code and your compiled report for
evaluation by your peers, just like the final projects in the prior courses.
Your submission will be reviewed by three of your peers, and
you in turn will review the work of three of your peers using the rubrics provided.
>> Then you'll repeat this process with another quiz and then,
a final peer assessment.
For the second round, you will need to do more advanced modeling.
However, the process and
logistics will be very similar to the first round of assessments.
In your final write-up, you should provide a summary of the data and problem and
discuss objectives for your models.
You're welcome to use Frequentist or Bayesian approaches, as you see fit.
We very strongly encourage you to try both and compare and contrast your findings.
Your interpretations should especially discuss how housing prices change with
changes in covariance.
You might also want to evaluate the predictive success of your models and
provide a discussion around that.
Lastly, it is a good idea to include a summary of limitations of your models and
methods, as well as what you would do if you had more time,
based on what you have discovered so far.
>> Along the way, you might feel like you take two steps forward and one step back.
This is entirely natural.
So please do not get discouraged.
Data analysis is an iterative process.
You might have some hypotheses about the relationships
between the variables you're working with and the only
way to find out if these hypotheses are supported by your data is to explore them.
Depending on what you discover during the exploration,
you might decide not to include it in your final project.
This does not mean you wasted time.
In fact, if every single visualization you made and every model you
fit makes it into your final project, you're probably making a mistake.
You want to try a variety of approaches and then pick and
choose between them to craft your final project.
That being said, you probably do not want to
constantly feel like you're going down the wrong path or
having to backtrack too far in your work due to a careless mistake.
In order to avoid this, we strongly recommend that you take cues from
the answers to the quiz questions and peer assessment feedback along the way.
>> Good luck with your analysis, and don't hesitate to ask questions and
share your findings in the course forum.
Keep in mind, there is no one right answer and your justifications and conclusions
are just as important as the methods and techniques you use to arrive at them.