Task 2: The data set asign2_customers.sas7bdat contains the details (city of residence, gender, customer ID, date of birth (DOB), and enrollment date (EnrolDate) of 49,999 registered customers. Each record has the following variables: Variable Name Description CustID Customer’s identity. City Customer’s city of residence Gender Gender of the customer (3 levels plus missing values, length of 6 bytes) DOB Customer’s date of birth (in SAS date values) EnrolDate Customer’s enrollment date (in SAS date values) Create a new SAS data set that contains the following characteristics: • Name the new data set Customers_New and keep it in the SAS library created in Task 1. • Replace all Gender values (including missing ones if any), except the ‘Male’ and the ’Female' values, with ‘Unknown’. Name the variable containing the new gender values as Gender. • Show each customer's age in full years on 31 Jul 2022 inclusive. For example, if a customer was born on 1 August 1998, then the customer’s age is 23. Name the variable containing the computed age value as Age. • Show each customer’s tenure in the number of days between the customer’s enrollment date and 31 Jul 2022 inclusive. For example, if a customer was enrolled on 29 Jun 2022, the customer’s tenure is 3 days. Name the variable containing the tenure values as Tenure. • Only these variables are contained in Customers_New: City of residence, customer’s identity, date of birth, enrollment date, gender, age, and length of tenure     There is some of raw data provided :       City               Gender              CustID                        DOB                        EnrolDate 1 Seatle         MFMF                  10292                         10/15/1964                 08/17/2017 2 Seatle           MFMF                47965                          02/12/1967             08/16/2017 3 New York      Female              35046                         05/19/1961               08/14/2017 4 Chicago          Male                 49234                        01/08/1986              08/13/2017 5 Seatle                                          42466                     06/06/1979             08/13/2017 6 Seatle              Male                   38316                     03/11/1949               08/13/2017 7 Seatle             MFMF                  33719                    08/22/2012               08/12/2017 8 Seatle              Female              34072                    10/18/1945               08/12/2017 9 Los Angeles     Male                  46769                   10/11/1952                08/12/2017 10 Chicago            Male                 46247                   06/20/2000            08/11/2017   ... View more