The data shown in Table $1$ below and the output in Table $2$ are based on a subset of a dataset on

cosmetic purchases $($Cosmetics$.$csv$)$ at a large chain drugstore. The store wants to analyze associations

among purchases of these items for purposes of point$-$of$-$sale display, guidance to sales personnel in

promoting cross$-$sales, and guidance for piloting an eventual time$-$of$-$purchase electronic

recommender system to boost cross$-$sales. Consider first only the data shown in Table $1,$ given in

binary matrix form.

Table $1$: Excerpt from data on cosmetics purchases in binary matrix form

Trans.

#

Bag Blush Nail

Polish

Brushes Concealer Eyebrow

Pencils

Bronzer

$10111101$

$20010101$

$30100111$

$40011101$

$50100101$

$60000100$

$70111101$

$80011001$

$90000100$

$101111000$

$110010001$

$120011101$

MBA $739$ Advanced Analytics

$2$

Table $2$: Association rules for cosmetics purchases data

Questions

a$.$ Consider the results of the association rules analysis shown in Table $2$

i$.$ For the first row, explain the $$confidence$$ output and how it is calculated.

ii$.$ For the first row, explain the $$support$$ output and how it is calculated.

iii. For the first row, explain the $$lift$$ and how it is calculated.

iv$.$ For the first row, explain the rule that is represented there in words.

b$.$ Now, use the complete dataset on the cosmetics purchases $($in the file Cosmetics.csv$).$ Using

the file in RStudio cloud, apply association rules to these data $($use the default parameters$).$

i$.$ Interpret the first three rules in the output in words.