Some Notes on Session 4

Hi all,

A few quick points.

1. Optional R tutorial this Sunday?

Many of you have said in the written quick-check feedback that an opt-in tutorial would help. How about an hourlong tutorial this sunday at, say 4 pm? Interested folks, pls bring your laptops with R installed and LAN cables. R installation instructions are mentioned in this previous blogpost.I'll intimate the venue (most likely AC8 LT since it has R already installed) as soon as I can get confirmation.

Pls write and let me know if the time is not convenient to a majority of you for any reason.

P.S.
Let me sweeten the tutorial deal a little bit. While walking you through how R works, I will *solve* the session 4 Homework with you. You are free to directly use the results for your homework submission.:)

P.P.S.
BTW, here are some excellent video tutorials on R that cover most newbie FAQs. If you can't find the answer to your query there, then feel free to contact me or my RA Mr Ankit Anand directly.

How to do stuff in r in two minutes or less

UC Denver site with video compilations of how to do basic stuff on R

2. Project Proposals provisionally accepted

I went through a few (not all, yet) project proposals. Interesting perspectives on some of them, tweaking needed on some R.O.s. Some others, the tools might need a rethink. But overall, good show.

Must mention that a few teams did not stick to the format prescribed (and described in such careful detail). No titles, verbose R.O.s, the management problem conflated with the DP, absence of plausible alternative DPs, etc. But the majority of proposals I saw seemed OK.

For the record, pls assume that you have provisional approval for your project proposals. If there's any issue, I'll contact you directly. I shall also putup some good (example) projects from previous years. Should help accelarate learning based on what worked and didn't in the past.

3. Rationale for Session 4 homeworks

Let's dissect the session 4 HW in some more detail.

Perceptual mapping Homework

Split core courses dataset into two by engineering versus non-engineering background. Save the split datasets in a separate excel worksheet.

For each split part of the dataset, bring data into the input format needed for MEXL or R. Thus, you will get two 4x4 tables with average scores for each attribute on each course offering. And the corresponding preference tables.

Now run the analysis. Save the plots on a PPT.

Examine the perceptual maps to see if something “jumps” out at you in terms of perceptual differences that can be interesting/usable from a Marketing standpoint. Record your observations as bullet points on a slide. Try to go a little beyond the obvious; dig beneath the surface phenomena and see what insights might come.

The exact same thing repeats for work-experience based segmentation. Now sort the dataset by work-ex length. Split the original dataset into two - those with workex below the median and those with workex above it. Repeat the exact same thing we did above.

Factor analysis

Read in the psychographic responses dataset into R. Run the factor analysis code as shown in the Session 4 R code blogpost. Answer the following questions:

(i)How many factors are optimal? How did you arrive at this decision?
(ii) How much of variance is explained by the optimal no. of factors?
(iii) Map which variables load onto which factors.
Now this part is really important:
(iv)Interpret what the factors may mean (i.e. what may be the underlying construct behind the variables). Give the factor a suitable name.
(iv) For those attending the R tutorial this sunday: We'll also see how to read, interpret and save the factor loadings and the factor scores from factor analysis. The factor scores can be used as 'reduced data' for downstream analysis.

Well, that's it from me for now. Hope to see you Sunday.

Sudhir