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the OregonPUMSrand.csv data

For this project, we’re going to use cluster analysis to tell a story about our data. I’m asking you to divide the Oregonians in your sample into groups based on two variables.

Please note this project will likely take some trial and error. Please relax into it and have some fun with the process: think of it as an exploration. Trial and error is the spice of life!

You will begin by taking a random subset of the OregonPUMSrand.csv data. It’s the same data you’ve seen before, with an extra column to allow randomization. You will take a small subset of this data (I recommend n=400, so as not to upset Statgraphics too much).

 

Process:

Step 1: Pick your sample (use Excel to sort>RANDOM, then take the top 400 rows of this “shuffled” data set and save them)

Step 2: Import that smaller file into Statgraphics (File>Open>Open Data Source>External Data File)

Step 3: Open the clustering dialog in Statgraphics (Describe>Multivariate Methods>Cluster Analysis)

Step 4: Choose your two variables. I recommend you choose continuous variables.

Step 5: Click OK.

Step 6: Pick your settings (I recommend starting with the default settings and 4 clusters) and click OK.

Step 7: Choose default settings plus 2D scatterplot and click OK

Step 8: Click on your scatterplot to make it bigger. Right click and choose Graphics Options and Pane Options to prettify your graph (larger points, circle clusters, labels, etc). Save that graph in a word document.

Step 9: Right click and choose Analysis Options to change your choices in Step 6.

Step 10: Keep repeating Step 9 until you like several of your graphs!

Step 11: Write up your project!

 

For your final report, include and explain your clustering. You may choose to use your Statgraphics output or use Tableau/Excel/other software to make a prettier graph. Tell your story, and how the clustering supports that story. Who are these groups? What does this clustering tell us about the people in Oregon? How did your thinking change as you refined your cluster analysis?

To really impress, give a little flavor! Describe a set of particular individuals who exemplify each cluster!.

 

Rubric for Project (40 points)

8 points: at least 3 different graphs, all using the same basic variables (Step 4) but different clustering choices (Step 10). Data process and data product both discussed.

8 points: Instructor’s subjective take on the product story. Was it gripping, interesting, well done?

8 points: conventions: correct punctuation, sentences, etc.

8 points: graph conventions, labels, etc.

8 points: your narration of the progression of your thinking (data process story).

Rubric

Project 2: Clustering Rubric

Project 2: Clustering Rubric
Criteria Ratings Pts
This criterion is linked to a Learning Outcome3 Different Clusterings
8.0 pts

Full Marks

4.0 pts

2 Clusterings

2.0 pts

One Clustering

0.0 pts

No Marks

8.0 pts
This criterion is linked to a Learning OutcomeProduct Story
8.0 pts

Full Marks

4.0 pts

Partial Credit

0.0 pts

No Marks

8.0 pts
This criterion is linked to a Learning OutcomeProcess Story
8.0 pts

Full Marks

4.0 pts

Partial Credit

0.0 pts

No Marks

8.0 pts
This criterion is linked to a Learning OutcomeWriting Mechanics
8.0 pts

Full Marks

6.0 pts

“Picky Problems”

4.0 pts

Partial Credit

0.0 pts

No Marks

8.0 pts
This criterion is linked to a Learning OutcomeGraphing Mechanics
8.0 pts

Full Marks

6.0 pts

“Picky Problems”

4.0 pts

Partial Credit

0.0 pts

No Marks

8.0 pts
Total Points: 40.0

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