Sales of Child Car Seats#

A simulated data set containing sales of child car seats at 400 different stores.

  • Sales: Unit sales (in thousands) at each location

  • CompPrice: Price charged by competitor at each location

  • Income: Community income level (in thousands of dollars)

  • Advertising: Local advertising budget for company at each location (in thousands of dollars)

  • Population: Population size in region (in thousands)

  • Price: Price company charges for car seats at each site

  • ShelveLoc: A factor with levels Bad, Good and Medium indicating the quality of the shelving location for the car seats at each site

  • Age: Average age of the local population

  • Education: Education level at each location

  • Urban: A factor with levels No and Yes to indicate whether the store is in an urban or rural location

  • US: A factor with levels No and Yes to indicate whether the store is in the US or not

from ISLP import load_data
Carseats = load_data('Carseats')
Carseats.columns

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

Carseats.shape

(400, 11)

Carseats.columns

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

Carseats.describe().iloc[:,:4]
Sales CompPrice Income Advertising
count 400.000000 400.000000 400.000000 400.000000
mean 7.496325 124.975000 68.657500 6.635000
std 2.824115 15.334512 27.986037 6.650364
min 0.000000 77.000000 21.000000 0.000000
25% 5.390000 115.000000 42.750000 0.000000
50% 7.490000 125.000000 69.000000 5.000000
75% 9.320000 135.000000 91.000000 12.000000
max 16.270000 175.000000 120.000000 29.000000