432.Can Advanced SAS Programming Detect, Clean, and Optimize Deep-Sea Exploration Vehicle Data While Identifying Fraud Patterns?

Advanced SAS Programming for Deep-Sea Vehicle Data Optimization and Fraud Detection

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HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —DATA | SET | MERGE | INPUT | DATALINES | IF  |  PROC SORT | PROC SQL | PROC APPEND | PROC TRANSPOSE | PROC DATASETS | RUN | QUIT | %MACRO | %MEND | FUNCTIONS ETC

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Table of Contents

1. Introduction
2. Business Context
3. Dataset Design
4. Initial Dataset Creation
5. Error Identification
6. Data Cleaning (Numeric Fixes)
7. Character Standardization
8. Date Handling (MDY, INTCK, INTNX)
9. Fraud Detection Macro
10. Data Transformation using SET
11. MERGE for Enrichment
12. PROC SQL Analysis
13. PROC APPEND
14. PROC TRANSPOSE
15. Utilization Classification
16. PROC DATASETS DELETE
17. Final Clean Dataset Code
18. 20 Key points About The Project
19. Summary
20. Conclusion

1. Introduction

Deep-sea exploration is one of the most technically demanding fields in modern science. Vehicles used in these missions—such as submersibles and remotely operated vehicles (ROVs)—generate massive volumes of operational and sensor data. This data must be accurate, consistent, and validated, especially when used for scientific research, defense, or commercial exploration.

However, real-world datasets are rarely perfect. They often contain:

• Missing values
• Incorrect formats
• Outliers and anomalies
• Fraudulent or manipulated entries

In this project, we will simulate a deep-sea exploration vehicle dataset, intentionally introduce errors, and then use Advanced SAS Programming techniques to clean, validate, transform, and analyze the data.

This is designed as a real-world, interview-ready SAS project with:

• 12+ independent SAS programs
• Macros for fraud detection
• Usage of DATA step, PROC SQL, PROC TRANSPOSE, PROC DATASETS, etc.
• Numeric and character functions
• Date handling with MDY, INTCK, INTNX
• Error detection and correction

2. Business Context

Deep-sea companies operate vehicles costing millions of dollars. Incorrect data may lead to:

• Wrong mission decisions
• Equipment damage
• Financial fraud
• Regulatory issues

This project simulates:

• Vehicle performance tracking
• Mission analytics
• Fraud detection (fake data entries, unrealistic values)

3. Dataset Design

Variables:

• Vehicle_Name
• Dive_Depth (meters)
• Pressure_Resistance (bars)
• Mission_Duration (hours)
• Data_Collected_GB
• Navigation_Error (%)
• Price (USD)
• Utilization_Status
• Mission_Start_Date
• Mission_End_Date 

4. Initial Dataset Creation (With Intentional Errors)

data deep_sea_raw;

infile datalines truncover;

input Vehicle_Name &$25. Dive_Depth Pressure_Resistance Mission_Duration 

      Data_Collected_GB Navigation_Error Price Mission_Start_Date : mmddyy10.;

format Mission_Start_Date mmddyy10.;

datalines;

nautilus  11000 1200 48 500 2.5 5000000 01/15/2025

abyssX  -500 1500 72 700 . 6000000 02/10/2025

deep explorer  9000 abc 36 450 1.2 4500000 03/05/2025

poseidon  12000 1800 60 -200 0.5 8000000 04/20/2025

triton  10000 1600 50 600 150 7000000 05/25/2025

;

run;

LOG:

NOTE: Invalid data for Pressure_Resistance in line 77 21-23.

RULE: ----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0

77 deep explorer 9000 abc 36 450 1.2 4500000 03/05/2025

Vehicle_Name=deep explorer Dive_Depth=9000 Pressure_Resistance=. Mission_Duration=36 Data_Collected_GB=450 Navigation_Error=1.2

Price=4500000 Mission_Start_Date=03/05/2025 _ERROR_=1 _N_=3

NOTE: The data set WORK.DEEP_SEA_RAW has 5 observations and 8 variables.

Explanation

This is our raw dataset, intentionally filled with errors:

·  Negative Dive_Depth

·  Character value “abc” in numeric field

·  Missing Navigation_Error

·  Negative Data_Collected_GB

·  Unrealistic Navigation_Error (150%)

Why Used

DATA step is the foundation of SAS for dataset creation.

Comparison of Input Modifiers

Modifier	Behavior	Use Case
$	Reads one word	Simple names
:	Reads until delimiter	Risky with spaces
&	Reads multiple words	Best for names with spaces

5. Error Identification

proc print data=deep_sea_raw;

run;

OUTPUT:

Obs	Vehicle_Name	Dive_Depth	Pressure_Resistance	Mission_Duration	Data_Collected_GB	Navigation_Error	Price	Mission_Start_Date
1	nautilus	11000	1200	48	500	2.5	5000000	01/15/2025
2	abyssX	-500	1500	72	700	.	6000000	02/10/2025
3	deep explorer	9000	.	36	450	1.2	4500000	03/05/2025
4	poseidon	12000	1800	60	-200	0.5	8000000	04/20/2025
5	triton	10000	1600	50	600	150.0	7000000	05/25/2025

Explanation

Used to visually inspect dataset.

Points

·  Quick validation

·  Identifies obvious anomalies

6. Numeric Data Cleaning

data clean_numeric;

set deep_sea_raw;

if Dive_Depth < 0 then Dive_Depth = abs(Dive_Depth);

if Data_Collected_GB < 0 then Data_Collected_GB = .;

if Navigation_Error = . then Navigation_Error = 0;

run;

proc print data=clean_numeric;

run;

OUTPUT:

Obs	Vehicle_Name	Dive_Depth	Pressure_Resistance	Mission_Duration	Data_Collected_GB	Navigation_Error	Price	Mission_Start_Date
1	nautilus	11000	1200	48	500	2.5	5000000	01/15/2025
2	abyssX	500	1500	72	700	0.0	6000000	02/10/2025
3	deep explorer	9000	.	36	450	1.2	4500000	03/05/2025
4	poseidon	12000	1800	60	.	0.5	8000000	04/20/2025
5	triton	10000	1600	50	600	150.0	7000000	05/25/2025

Explanation

Fixes:

·  Negative values using abs()

·  Missing values replaced

Numeric Functions Used:

·  abs()

Why Used

Ensures numeric consistency.

7. Character Functions Standardization

data clean_char;

set clean_numeric;

Vehicle_Name = propcase(strip(Vehicle_Name));

Vehicle_Name = catx('_', Vehicle_Name, 'Vehicle');

run;

proc print data=clean_char;

run;

OUTPUT:

Obs	Vehicle_Name	Dive_Depth	Pressure_Resistance	Mission_Duration	Data_Collected_GB	Navigation_Error	Price	Mission_Start_Date
1	Nautilus_Vehicle	11000	1200	48	500	2.5	5000000	01/15/2025
2	Abyssx_Vehicle	500	1500	72	700	0.0	6000000	02/10/2025
3	Deep Explorer_Vehicle	9000	.	36	450	1.2	4500000	03/05/2025
4	Poseidon_Vehicle	12000	1800	60	.	0.5	8000000	04/20/2025
5	Triton_Vehicle	10000	1600	50	600	150.0	7000000	05/25/2025

Functions Used

·  strip() → removes spaces

·  propcase() → proper capitalization

·  catx() → concatenation

Why Used

Standard naming improves reporting.

8. Date Handling

data date_handling;

set clean_char;

Mission_End_Date = intnx('day', Mission_Start_Date, Mission_Duration);

Mission_Length = intck('day', Mission_Start_Date, Mission_End_Date);

format Mission_End_Date mmddyy10.;

run;

proc print data=date_handling;

run;

OUTPUT:

Obs	Vehicle_Name	Dive_Depth	Pressure_Resistance	Mission_Duration	Data_Collected_GB	Navigation_Error	Price	Mission_Start_Date	Mission_End_Date	Mission_Length
1	Nautilus_Vehicle	11000	1200	48	500	2.5	5000000	01/15/2025	03/04/2025	48
2	Abyssx_Vehicle	500	1500	72	700	0.0	6000000	02/10/2025	04/23/2025	72
3	Deep Explorer_Vehicle	9000	.	36	450	1.2	4500000	03/05/2025	04/10/2025	36
4	Poseidon_Vehicle	12000	1800	60	.	0.5	8000000	04/20/2025	06/19/2025	60
5	Triton_Vehicle	10000	1600	50	600	150.0	7000000	05/25/2025	07/14/2025	50

8.

Functions

·  INTNX → add duration

·  INTCK → calculate difference

 Why Used

Critical for mission timeline analysis.

9. Fraud Detection Macro

%macro fraud_check;

data fraud_flag;

set date_handling;

if Navigation_Error > 100 then Fraud_Flag = 'YES';

else if Dive_Depth > 11000 then Fraud_Flag = 'YES';

else Fraud_Flag = 'NO';

run;

proc print data=fraud_flag;

run;

%mend;

%fraud_check;

OUTPUT:

Obs	Vehicle_Name	Dive_Depth	Pressure_Resistance	Mission_Duration	Data_Collected_GB	Navigation_Error	Price	Mission_Start_Date	Mission_End_Date	Mission_Length	Fraud_Flag
1	Nautilus_Vehicle	11000	1200	48	500	2.5	5000000	01/15/2025	03/04/2025	48	NO
2	Abyssx_Vehicle	500	1500	72	700	0.0	6000000	02/10/2025	04/23/2025	72	NO
3	Deep Explorer_Vehicle	9000	.	36	450	1.2	4500000	03/05/2025	04/10/2025	36	NO
4	Poseidon_Vehicle	12000	1800	60	.	0.5	8000000	04/20/2025	06/19/2025	60	YES
5	Triton_Vehicle	10000	1600	50	600	150.0	7000000	05/25/2025	07/14/2025	50	YES

Explanation

Flags suspicious entries.

Why Used

Reusable logic across datasets.

10. SET Statement Usage

data combined_data;

set fraud_flag;

run;

proc print data=combined_data;

run;

OUTPUT:

Obs	Vehicle_Name	Dive_Depth	Pressure_Resistance	Mission_Duration	Data_Collected_GB	Navigation_Error	Price	Mission_Start_Date	Mission_End_Date	Mission_Length	Fraud_Flag
1	Nautilus_Vehicle	11000	1200	48	500	2.5	5000000	01/15/2025	03/04/2025	48	NO
2	Abyssx_Vehicle	500	1500	72	700	0.0	6000000	02/10/2025	04/23/2025	72	NO
3	Deep Explorer_Vehicle	9000	.	36	450	1.2	4500000	03/05/2025	04/10/2025	36	NO
4	Poseidon_Vehicle	12000	1800	60	.	0.5	8000000	04/20/2025	06/19/2025	60	YES
5	Triton_Vehicle	10000	1600	50	600	150.0	7000000	05/25/2025	07/14/2025	50	YES

Explanation

Creates new dataset.

Why Used

Basic dataset copying and transformation.

11. MERGE Example

data pricing;

input Vehicle_Name:$25. Price_Category $;

datalines;

Nautilus_Vehicle High

Poseidon_Vehicle Premium

;

run;

proc print data=pricing;

run;

OUTPUT:

Obs	Vehicle_Name	Price_Category
1	Nautilus_Vehicle	High
2	Poseidon_Vehicle	Premium

proc sort data=combined_data;by Vehicle_Name;run;

proc print data=combined_data;

run;

OUTPUT:

Obs	Vehicle_Name	Dive_Depth	Pressure_Resistance	Mission_Duration	Data_Collected_GB	Navigation_Error	Price	Mission_Start_Date	Mission_End_Date	Mission_Length	Fraud_Flag
1	Abyssx_Vehicle	500	1500	72	700	0.0	6000000	02/10/2025	04/23/2025	72	NO
2	Deep Explorer_Vehicle	9000	.	36	450	1.2	4500000	03/05/2025	04/10/2025	36	NO
3	Nautilus_Vehicle	11000	1200	48	500	2.5	5000000	01/15/2025	03/04/2025	48	NO
4	Poseidon_Vehicle	12000	1800	60	.	0.5	8000000	04/20/2025	06/19/2025	60	YES
5	Triton_Vehicle	10000	1600	50	600	150.0	7000000	05/25/2025	07/14/2025	50	YES

proc sort data=pricing;by Vehicle_Name;run;

proc print data=pricing;

run;

OUTPUT:

Obs	Vehicle_Name	Price_Category
1	Nautilus_Vehicle	High
2	Poseidon_Vehicle	Premium

Explanation

Before using a MERGE statement with a BY variable, SAS requires both datasets to be sorted by that variable. Here, Vehicle_Name is the key used to combine records from combined_data and pricing.

Why It Is Used

·  Ensures correct row-to-row matching

·  Prevents data misalignment

·  Avoids SAS warnings like:

          ERROR: BY variables are not properly sorted

data merged_data;

merge combined_data 

      pricing;

by Vehicle_Name;

if price < 5000000 then Price_Category="Moderate";

else if 5000000 <= Price < 8000000 then Price_Category="High";

else if Price_Category="Premium";

run;

proc print data=merged_data;

run;

OUTPUT:

Obs	Vehicle_Name	Dive_Depth	Pressure_Resistance	Mission_Duration	Data_Collected_GB	Navigation_Error	Price	Mission_Start_Date	Mission_End_Date	Mission_Length	Fraud_Flag	Price_Category
1	Abyssx_Vehicle	500	1500	72	700	0.0	6000000	02/10/2025	04/23/2025	72	NO	High
2	Deep Explorer_Vehicle	9000	.	36	450	1.2	4500000	03/05/2025	04/10/2025	36	NO	Moderate
3	Nautilus_Vehicle	11000	1200	48	500	2.5	5000000	01/15/2025	03/04/2025	48	NO	High
4	Poseidon_Vehicle	12000	1800	60	.	0.5	8000000	04/20/2025	06/19/2025	60	YES	Premium
5	Triton_Vehicle	10000	1600	50	600	150.0	7000000	05/25/2025	07/14/2025	50	YES	High

Explanation

This logic categorizes vehicles into pricing tiers based on the numeric variable Price.

Why It Is Used

·  Creates a derived variable (Price_Category)

·  Converts continuous numeric data → categorical groups

·  Helps in:

·  Reporting

·  Analysis

·  Visualization

Why NOT use

5000000 < Price < 8000000

Because:

·  SAS does not support chained comparisons properly

·  It evaluates left to right → gives wrong results

Final Concept Summary

Concept	Why Used	Risk if Not Used
PROC SORT	Correct merging	Wrong data mapping
IF with AND	Accurate categorization	Logical errors

12. PROC SQL Analysis

proc sql;

select Vehicle_Name, avg(Data_Collected_GB) as Avg_Data

from merged_data

group by Vehicle_Name;

quit;

OUTPUT:

Vehicle_Name	Avg_Data
Abyssx_Vehicle	700
Deep Explorer_Vehicle	450
Nautilus_Vehicle	500
Poseidon_Vehicle	.
Triton_Vehicle	600

Why Used

Advanced querying.

13. PROC APPEND

proc append base=combined_data 

            data=merged_data force;

run;

proc print data=combined_data;

run;

OUTPUT:

Obs	Vehicle_Name	Dive_Depth	Pressure_Resistance	Mission_Duration	Data_Collected_GB	Navigation_Error	Price	Mission_Start_Date	Mission_End_Date	Mission_Length	Fraud_Flag
1	Abyssx_Vehicle	500	1500	72	700	0.0	6000000	02/10/2025	04/23/2025	72	NO
2	Deep Explorer_Vehicle	9000	.	36	450	1.2	4500000	03/05/2025	04/10/2025	36	NO
3	Nautilus_Vehicle	11000	1200	48	500	2.5	5000000	01/15/2025	03/04/2025	48	NO
4	Poseidon_Vehicle	12000	1800	60	.	0.5	8000000	04/20/2025	06/19/2025	60	YES
5	Triton_Vehicle	10000	1600	50	600	150.0	7000000	05/25/2025	07/14/2025	50	YES
6	Abyssx_Vehicle	500	1500	72	700	0.0	6000000	02/10/2025	04/23/2025	72	NO
7	Deep Explorer_Vehicle	9000	.	36	450	1.2	4500000	03/05/2025	04/10/2025	36	NO
8	Nautilus_Vehicle	11000	1200	48	500	2.5	5000000	01/15/2025	03/04/2025	48	NO
9	Poseidon_Vehicle	12000	1800	60	.	0.5	8000000	04/20/2025	06/19/2025	60	YES
10	Triton_Vehicle	10000	1600	50	600	150.0	7000000	05/25/2025	07/14/2025	50	YES

Why Used

Adds records efficiently.

14. PROC TRANSPOSE

proc transpose data=combined_data out=transposed;

var Dive_Depth Data_Collected_GB;

run;

proc print data=transposed;

run;

OUTPUT:

Obs	_NAME_	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10
1	Dive_Depth	500	9000	11000	12000	10000	500	9000	11000	12000	10000
2	Data_Collected_GB	700	450	500	.	600	700	450	500	.	600

Why Used

Reshapes dataset.

15. Utilization Classification

data utilization;

set combined_data;

length Utilization_Status $10.;

if Mission_Duration > 60 then Utilization_Status='High';

else if Mission_Duration > 40 then Utilization_Status='Medium';

else Utilization_Status='Low';

run;

proc print data=utilization;

run;

OUTPUT:

Obs	Vehicle_Name	Dive_Depth	Pressure_Resistance	Mission_Duration	Data_Collected_GB	Navigation_Error	Price	Mission_Start_Date	Mission_End_Date	Mission_Length	Fraud_Flag	Utilization_Status
1	Abyssx_Vehicle	500	1500	72	700	0.0	6000000	02/10/2025	04/23/2025	72	NO	High
2	Deep Explorer_Vehicle	9000	.	36	450	1.2	4500000	03/05/2025	04/10/2025	36	NO	Low
3	Nautilus_Vehicle	11000	1200	48	500	2.5	5000000	01/15/2025	03/04/2025	48	NO	Medium
4	Poseidon_Vehicle	12000	1800	60	.	0.5	8000000	04/20/2025	06/19/2025	60	YES	Medium
5	Triton_Vehicle	10000	1600	50	600	150.0	7000000	05/25/2025	07/14/2025	50	YES	Medium
6	Abyssx_Vehicle	500	1500	72	700	0.0	6000000	02/10/2025	04/23/2025	72	NO	High
7	Deep Explorer_Vehicle	9000	.	36	450	1.2	4500000	03/05/2025	04/10/2025	36	NO	Low
8	Nautilus_Vehicle	11000	1200	48	500	2.5	5000000	01/15/2025	03/04/2025	48	NO	Medium
9	Poseidon_Vehicle	12000	1800	60	.	0.5	8000000	04/20/2025	06/19/2025	60	YES	Medium
10	Triton_Vehicle	10000	1600	50	600	150.0	7000000	05/25/2025	07/14/2025	50	YES	Medium

Why Used

Removes temporary datasets.

16. PROC DATASETS DELETE

proc datasets library=work;

delete clean_numeric clean_char;

quit;

LOG:

NOTE: Deleting WORK.CLEAN_NUMERIC (memtype=DATA).

NOTE: Deleting WORK.CLEAN_CHAR (memtype=DATA).

17. Final Corrected Dataset Code (Full Length)

data final_dataset;

set deep_sea_raw;

Dive_Depth = abs(Dive_Depth);

if Data_Collected_GB < 0 then Data_Collected_GB = .;

if Navigation_Error = . then Navigation_Error = 0;

Vehicle_Name = propcase(strip(Vehicle_Name));

Vehicle_Name = catx('_', Vehicle_Name, 'Vehicle');

Mission_End_Date = intnx('day', Mission_Start_Date, Mission_Duration);

Mission_Length = intck('day', Mission_Start_Date, Mission_End_Date);

if Navigation_Error > 100 or Dive_Depth > 11000 then Fraud_Flag='YES';

else Fraud_Flag='NO';

length Utilization_Status $10.;

if Mission_Duration > 60 then Utilization_Status='High';

else if Mission_Duration > 40 then Utilization_Status='Medium';

else Utilization_Status='Low';

format Mission_End_Date mmddyy10.;

run;

proc print data=final_dataset;

run;

OUTPUT:

Obs	Vehicle_Name	Dive_Depth	Pressure_Resistance	Mission_Duration	Data_Collected_GB	Navigation_Error	Price	Mission_Start_Date	Mission_End_Date	Mission_Length	Fraud_Flag	Utilization_Status
1	Nautilus_Vehicle	11000	1200	48	500	2.5	5000000	01/15/2025	03/04/2025	48	NO	Medium
2	Abyssx_Vehicle	500	1500	72	700	0.0	6000000	02/10/2025	04/23/2025	72	NO	High
3	Deep Explorer_Vehicle	9000	.	36	450	1.2	4500000	03/05/2025	04/10/2025	36	NO	Low
4	Poseidon_Vehicle	12000	1800	60	.	0.5	8000000	04/20/2025	06/19/2025	60	YES	Medium
5	Triton_Vehicle	10000	1600	50	600	150.0	7000000	05/25/2025	07/14/2025	50	YES	Medium

18. 20 Key Points About The Project

1.     Designed a high-quality simulated dataset representing deep-sea exploration vehicles with operational and financial variables.

2.     Introduced real-world data issues such as missing values, invalid entries, and outliers to mimic production-level challenges.

3.     Applied DATA step techniques to ingest, structure, and pre-process raw mission data efficiently.

4.     Utilized robust INPUT methods (&, informats, TRUNCOVER) to correctly parse complex character fields like multi-word vehicle names.

5.     Implemented numeric data validation using functions like ABS, conditional checks, and missing value handling.

6.     Standardized character variables using STRIP, PROPCASE, UPCASE, and LOWCASE for consistency across datasets.

7.     Leveraged concatenation functions (CAT, CATX) to create structured and readable derived variables.

8.     Applied date handling techniques using MDY, INTNX, and INTCK for mission timeline calculations and duration tracking.

9.     Detected and corrected data anomalies such as negative dive depths, unrealistic navigation errors, and invalid pressure values.

10.  Built reusable SAS macros to automate fraud detection logic based on predefined thresholds and business rules.

11.  Flagged suspicious records such as extreme navigation errors (>100%) and abnormal dive depths beyond engineering limits.

12.  Performed dataset transformations using SET statements for streamlined data flow and intermediate processing.

13.  Combined multiple datasets using MERGE with proper sorting to ensure accurate record alignment.

14.  Conducted advanced querying and aggregation using PROC SQL for analytical insights like average data collection.

15.  Appended incremental datasets using PROC APPEND to simulate real-time data integration scenarios.

16.  Reshaped data structures using PROC TRANSPOSE for reporting flexibility and downstream analytics.

17.  Classified vehicle utilization levels (Low, Medium, High) based on mission duration thresholds.

18.  Managed workspace efficiently by removing temporary datasets using PROC DATASETS DELETE.

19.  Ensured data integrity and readiness for reporting by applying end-to-end cleaning, validation, and transformation workflows.

20.  Delivered an interview-ready, production-style SAS project demonstrating data engineering, validation, and fraud detection expertise in a single pipeline.

Summary

In this project, we built a complete SAS workflow for deep-sea exploration data:

• Created dataset with intentional errors
• Applied cleaning using numeric & character functions
• Used date functions for mission tracking
• Built fraud detection macro
• Performed transformations using SET, MERGE
• Used PROC SQL, APPEND, TRANSPOSE
• Classified utilization

Conclusion

Working with real-world data is never as clean as textbooks make it seem. In this deep-sea exploration project, we saw how even a small dataset can contain multiple layers of errors from incorrect numeric values to inconsistent naming and unrealistic operational metrics. Using SAS, we didn’t just clean the data.we transformed it into something meaningful and reliable. The use of macros for fraud detection adds a powerful layer of automation, making the system scalable for large datasets. Functions like INTNX and INTCK helped us handle time-based logic efficiently, which is crucial in mission-based industries. Overall, this project reflects what actually happens in real jobs: identifying problems, fixing them systematically, and ensuring the final dataset is trustworthy. Mastering these techniques will definitely give you an edge in SAS interviews and real-world projects.

SAS INTERVIEW QUESTIONS

1. What is automatic macro variable?

Answer:
Automatic macro variables are system-created variables such as &SYSDATE, &SYSUSERID.

2. What is macro debugging?

Answer:
Macro debugging uses options like MPRINT, MLOGIC, SYMBOLGEN to track macro execution.

3. What is the difference between local and global macro variables?

Answer:
Global variables are available throughout the program, while local variables exist inside a macro only.

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About the Author:

SAS Learning Hub is a data analytics and SAS programming platform focused on clinical, financial, and real-world data analysis. The content is created by professionals with academic training in Pharmaceutics and hands-on experience in Base SAS, PROC SQL, Macros, SDTM, and ADaM, providing practical and industry-relevant SAS learning resources.

Disclaimer:

The datasets and analysis in this article are created for educational and demonstration purposes only. They do not represent DEEP-SEA VEHICLE data.

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