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 locationCompPrice
: Price charged by competitor at each locationIncome
: 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 siteShelveLoc
: A factor with levels Bad, Good and Medium indicating the quality of the shelving location for the car seats at each siteAge
: Average age of the local populationEducation
: Education level at each locationUrban
: A factor with levels No and Yes to indicate whether the store is in an urban or rural locationUS
: 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 |