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Sas performing basic sas programing

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need help with compling my sas assignments based mostly based on group by and do loops need help with 2,3,4,5 questions iam attaching only questions as the website is not accespting the data
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Econometrics 673 Fall 2022 Homework #3 Due in Canvas by midnight Friday, October 7th How Far do My Customers Travel The purpose of the analysis in this exercise is to perform a type of geographic market analysis commonly used in U.S. antitrust matters. Starting from a specific physical location (that is, a geographic sales point) how large of an area is necessary to capture 75% (or 90%) of sales for a location based on the distance customers travel to a location. From the class website, download the “HW3_Data.zip” ZIP file. There are two files in the compressed zip file, “hw3_data.sas7bdat” which is a SAS dataset, and “Location Lat Long Data.xlsx”. The first few rows of the SAS dataset should look like the following: There are 27 unique values for “location_id” and in total there should be 5,217,038 observations in these data. This data is transactions sales data for a retail establishment based on a loyalty card program. The variable “Customer_ID” is a unique identifier for a specific loyalty card member. The variables “latitude” and “longitude” are the physical latitude and longitude of customer’s home address; “quantity” is the quantity purchased; and “date” is the transaction date (stored as a SAS date value). The first few rows of the “Location Lat Long Data” look like: 1. To determine how far customers travel to reach the specific retailing location from which these data were recorded, merge the “Location Lat Long Data.xlsx” data into the transaction data by location_id. (See the Lecture 6 PP slides for accessing Excel data with a LIBNAME statement, or the Lecture 7 slides for using PROC IMPORT.) The “ref_latitude” and “ref_longitude” variables are the physical latitude and longitude for the retail location – location_id; latitude and longitude from the sales data are the customer’s home location. Now use the GEODIST function to calculate the straight-line distance (measured in miles) traveled by each customer to the retail location: a. Miles=GEODIST(latitude, longitude, ref_latitude, ref_longitude, ‘M’); 2. Next use accumulator variables and by-group processing to aggregate (sum) the quantity variable for each location_id/customer_id combination and also get an overall total for each location_id. a. Create a subset data set where, for each location_id, each observation on customer_id is unique and includes the customer level aggregate of the quantity variable. b. Create another subset data set of these data that includes the location_id and the overall total quantity for each location_id. 3. Using the distances (Miles), create a distance class variable for purposes of constructing frequency distributions and cumulative distributions (counts and percentages) for each location_id to show the proportion of sales associated with travel distances in half mile increments up to 100 miles. For example: a. Dist_class=0; b. If 0
Econometrics 673 Fall 2022 Homework #3 Due in Canvas by midnight Friday, October 7th How Far do My Customers Travel The purpose of the analysis in this exercise is to perform a type of geographic market analysis commonly used in U.S. antitrust matters. Starting from a specific physical location (that is, a geographic sales point) how large of an area is necessary to capture 75% (or 90%) of sales for a location based on the distance customers travel to a location. From the class website, download the “HW3_Data.zip” ZIP file. There are two files in the compressed zip file, “hw3_data.sas7bdat” which is a SAS dataset, and “Location Lat Long Data.xlsx”. The first few rows of the SAS dataset should look like the following: There are 27 unique values for “location_id” and in total there should be 5,217,038 observations in these data. This data is transactions sales data for a retail establishment based on a loyalty card program. The variable “Customer_ID” is a unique identifier for a specific loyalty card member. The variables “latitude” and “longitude” are the physical latitude and longitude of customer’s home address; “quantity” is the quantity purchased; and “date” is the transaction date (stored as a SAS date value). The first few rows of the “Location Lat Long Data” look like: 1. To determine how far customers travel to reach the specific retailing location from which these data were recorded, merge the “Location Lat Long Data.xlsx” data into the transaction data by location_id. (See the Lecture 6 PP slides for accessing Excel data with a LIBNAME statement, or the Lecture 7 slides for using PROC IMPORT.) The “ref_latitude” and “ref_longitude” variables are the physical latitude and longitude for the retail location – location_id; latitude and longitude from the sales data are the customer’s home location. Now use the GEODIST function to calculate the straight-line distance (measured in miles) traveled by each customer to the retail location: a. Miles=GEODIST(latitude, longitude, ref_latitude, ref_longitude, ‘M’); 2. Next use accumulator variables and by-group processing to aggregate (sum) the quantity variable for each location_id/customer_id combination and also get an overall total for each location_id. a. Create a subset data set where, for each location_id, each observation on customer_id is unique and includes the customer level aggregate of the quantity variable. b. Create another subset data set of these data that includes the location_id and the overall total quantity for each location_id. 3. Using the distances (Miles), create a distance class variable for purposes of constructing frequency distributions and cumulative distributions (counts and percentages) for each location_id to show the proportion of sales associated with travel distances in half mile increments up to 100 miles. For example: a. Dist_class=0; b. If 0
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