3.6.7 Qualitative Predictors

Here we use the Carseats data, which is included in the ISLP package.

In [35]: Carseats = load_data('Carseats')
Carseats.columns

Out[35]: Index(['Sales', 'CompPrice', 'Income', 'Advertising',
       'Population', 'Price', 'ShelveLoc', 'Age', 'Education',
       'Urban', 'US'],
      dtype='object')

The Carseats data includes qualitative predictors such as ShelveLoc , an indicator of the quality of the shelving location — that is, the space within a store in which the car seat is displayed.

encoding

In [36]: allvars = list(Carseats.columns.drop('Sales'))
y = Carseats['Sales']
final = allvars + [('Income', 'Advertising'),
                   ('Price', 'Age')]
X = MS(final).fit_transform(Carseats)
model = sm.OLS(y, X)
summarize(model.fit())

Out[36]:             coef  std err       t  P>|t|
intercept       6.5756   1.009   6.519  0.000

CompPrice 0.0929 0.004 22.567 0.000

`Income`	`0.0109`	`0.003`	`4.183`	`0.000`
`Advertising`	`0.0702`	`0.023`	`3.107`	`0.002`
`Population`	`0.0002`	`0.000`	`0.433`	`0.665`
`Price`	`-0.1008`	`0.007`	`-13.549`	`0.000`
`ShelveLoc[Good]`	`4.8487`	`0.153`	`31.724`	`0.000`
`ShelveLoc[Medium]`	`1.9533`	`0.126`	`15.531`	`0.000`
`Age`	`-0.0579`	`0.016`	`-3.633`	`0.000`
`Education`	`-0.0209`	`0.020`	`-1.063`	`0.288`
`Urban[Yes]`	`0.1402`	`0.112`	`1.247`	`0.213`
`US[Yes]`	`-0.1576`	`0.149`	`-1.058`	`0.291`
`Income:Advertising`	`0.0008`	`0.000`	`2.698`	`0.007`
`Price:Age`	`0.0001`	`0.000`	`0.801`	`0.424`

In the first line above, we made allvars a list, so that we could add the interaction terms two lines down.

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