Compute and report the characteristics of the affinity$-$based segment for Product $3$

$($our candidate as given above$).$ Report the characteristics in terms of the following

descriptors: IPr, Iin, ICp, ICl, ICn, IBr, I$*$pPr$30,$ I$*$pPr$10,$ I$*$pPr$05,$ I$*$pIn$0\mathrm{.}5,$ I$*$pIn$1,$

I$*$pIn$3,$ I$*$pCp$12,$ I$*$pCp$20,$ I$*$pCp$32,$ I$*$pClD$,$ I$*$pClF$,$ I$*$pClE$,$ I$*$pCnSl$,$ I$*$pCnSp$,$

I$*$pCnLk$,$ I$*$pBrA$,$ I$*$pBrB$,$ I$*$pBrC$,$ and the demographics income, age, sports and

gradschl. For this you need to compute the weighted average of all the columns in the

"for$-$cluster$-$analysis" worksheet, weighted by the probabilities given in column BF of

the "mugs$-$full" worksheet. You should not do this in Excel directly. You are to load

the data into python or R and compute the weighted averages in R or python; this is to

help you build familiarity with these languages and prepare you better for your job.

$2.$ Repeat the step above for the product of brand A and the product for brand B$.$

Compute also the overall mean for each descriptor $($this done by a simple average of

each descriptor across all $311$ customers$).$ Compute the log$-$lifts for all variables for

the affinity based segment for each product and focus on the large positive and

negative numbers to figure out how each segment is different from the other segments

and the overall population average. $($You will use these in the next step to come up

with a verbal description that characterizes each segment.$)$

$3.$ Use your findings from Step $2$ above to come up with a verbal description and a

persona story that characterizes each segment. The persona story gives a mental

image to the marketing manager not only in terms of the descriptors in the dataset,

but also in terms of plausible hypothesized characteristics that go beyond the

descriptors available.