Orange Juice Data#
The data contains 1070 purchases where the customer either purchased Citrus Hill or Minute Maid Orange Juice. A number of characteristics of the customer and product are recorded.
Purchase
: A factor with levels ‘CH’ and ‘MM’ indicating whether the customer purchased Citrus Hill or Minute Maid Orange JuiceWeekofPurchase
: Week of purchaseStoreID
: Store IDPriceCH
: Price charged for CHPriceMM
: Price charged for MMDiscCH
: Discount offered for CHDiscMM
: Discount offered for MMSpecialCH
: Indicator of special on CHSpecialMM
: Indicator of special on MMLoyalCH
: Customer brand loyalty for CHSalePriceMM
: Sale price for MMSalePriceCH
: Sale price for CHPriceDiff
: Sale price of MM less sale price of CHStore7
: A factor with levels ‘No’ and ‘Yes’ indicating whether the sale is at Store 7PctDiscMM
: Percentage discount for MMPctDiscCH
: Percentage discount for CHListPriceDiff
: List price of MM less list price of CHSTORE
: Which of 5 possible stores the sale occured at
Source#
Stine, Robert A., Foster, Dean P., Waterman, Richard P. Business Analysis Using Regression (1998). Published by Springer.
from ISLP import load_data
OJ = load_data('OJ')
OJ.columns
Index(['Purchase', 'WeekofPurchase', 'StoreID', 'PriceCH', 'PriceMM', 'DiscCH',
'DiscMM', 'SpecialCH', 'SpecialMM', 'LoyalCH', 'SalePriceMM',
'SalePriceCH', 'PriceDiff', 'Store7', 'PctDiscMM', 'PctDiscCH',
'ListPriceDiff', 'STORE'],
dtype='object')
OJ.shape
(1070, 18)
OJ.columns
Index(['Purchase', 'WeekofPurchase', 'StoreID', 'PriceCH', 'PriceMM', 'DiscCH',
'DiscMM', 'SpecialCH', 'SpecialMM', 'LoyalCH', 'SalePriceMM',
'SalePriceCH', 'PriceDiff', 'Store7', 'PctDiscMM', 'PctDiscCH',
'ListPriceDiff', 'STORE'],
dtype='object')
OJ.describe().iloc[:,:4]
WeekofPurchase | StoreID | PriceCH | PriceMM | |
---|---|---|---|---|
count | 1070.000000 | 1070.000000 | 1070.000000 | 1070.000000 |
mean | 254.381308 | 3.959813 | 1.867421 | 2.085411 |
std | 15.558286 | 2.308984 | 0.101970 | 0.134386 |
min | 227.000000 | 1.000000 | 1.690000 | 1.690000 |
25% | 240.000000 | 2.000000 | 1.790000 | 1.990000 |
50% | 257.000000 | 3.000000 | 1.860000 | 2.090000 |
75% | 268.000000 | 7.000000 | 1.990000 | 2.180000 |
max | 278.000000 | 7.000000 | 2.090000 | 2.290000 |