161.ULTIMATE GUIDE TO MASTERING BOOKSTORE SALES DATA ANALYSIS USING SAS | DATA | PROC CONTENTS | PROC PRINT | PROC MEANS | PROC FREQ | PROC SGPLOT | PROC SORT | PROC REPORT | PROC CORR | PROC REG | PROC EXPORT | DATA CLEANING TECHNIQUES

ULTIMATE GUIDE TO MASTERING BOOKSTORE SALES DATA ANALYSIS USING SAS | DATA | PROC CONTENTS | PROC PRINT | PROC MEANS | PROC FREQ | PROC SGPLOT | PROC SORT | PROC REPORT | PROC CORR | PROC REG | PROC EXPORT | DATA CLEANING TECHNIQUES

/*Creating a unique dataset centered around bookstore sales and demonstrating various SAS procedures using this dataset.*/

data bookstore_sales;

    length Date 8 Branch $20 Genre $20 Title $50 Author $30 Units_Sold 8 Revenue 8;

    format Date yymmdd10.;

    input Date : yymmdd10. Branch : $20. Genre : $20. Title & $50. Author & $30. Units_Sold Revenue;

    datalines;

2025-04-01 Hyderabad Fiction The Silent Patient  Alex Michaelides  5 1500

2025-04-01 Mumbai Non-Fiction Sapiens  Yuval Noah Harari  3 1800

2025-04-01 Delhi Science A Brief History of Time  Stephen Hawking  2 1000

2025-04-02 Hyderabad Fiction The Midnight Library  Matt Haig  4 1200

2025-04-02 Mumbai Fiction The Alchemist  Paulo Coelho  6 1800

;

run;

proc print;run;

Output:

Obs	Date	Branch	Genre	Title	Author	Units_Sold	Revenue
1	2025-04-01	Hyderabad	Fiction	The Silent Patient	Alex Michaelides	5	1500
2	2025-04-01	Mumbai	Non-Fiction	Sapiens	Yuval Noah Harari	3	1800
3	2025-04-01	Delhi	Science	A Brief History of Time	Stephen Hawking	2	1000
4	2025-04-02	Hyderabad	Fiction	The Midnight Library	Matt Haig	4	1200
5	2025-04-02	Mumbai	Fiction	The Alchemist	Paulo Coelho	6	1800

proc contents data=bookstore_sales;

run;

Output:

                                                              The CONTENTS Procedure

Data Set Name	WORK.BOOKSTORE_SALES	Observations	5
Member Type	DATA	Variables	7
Engine	V9	Indexes	0
Created	14/09/2015 00:05:59	Observation Length	144
Last Modified	14/09/2015 00:05:59	Deleted Observations	0
Protection		Compressed	NO
Data Set Type		Sorted	NO
Label
Data Representation	WINDOWS_64
Encoding	wlatin1 Western (Windows)

Engine/Host Dependent Information
Data Set Page Size	65536
Number of Data Set Pages	1
First Data Page	1
Max Obs per Page	454
Obs in First Data Page	5
Number of Data Set Repairs	0
ExtendObsCounter	YES
Filename	C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD8676_DESKTOP-QFAA4KV_\bookstore_sales.sas7bdat
Release Created	9.0401M2
Host Created	X64_8HOME

Alphabetic List of Variables and Attributes
#	Variable	Type	Len	Format
5	Author	Char	30
2	Branch	Char	20
1	Date	Num	8	YYMMDD10.
3	Genre	Char	20
7	Revenue	Num	8
4	Title	Char	50
6	Units_Sold	Num	8

proc print data=bookstore_sales;

    title "Bookstore Sales Data";

run;

Output:

Bookstore Sales Data

Obs	Date	Branch	Genre	Title	Author	Units_Sold	Revenue
1	2025-04-01	Hyderabad	Fiction	The Silent Patient	Alex Michaelides	5	1500
2	2025-04-01	Mumbai	Non-Fiction	Sapiens	Yuval Noah Harari	3	1800
3	2025-04-01	Delhi	Science	A Brief History of Time	Stephen Hawking	2	1000
4	2025-04-02	Hyderabad	Fiction	The Midnight Library	Matt Haig	4	1200
5	2025-04-02	Mumbai	Fiction	The Alchemist	Paulo Coelho	6	1800

proc means data=bookstore_sales mean median min max std;

    var Units_Sold Revenue;

run;

Output:

                                                                    The MEANS Procedure

Variable	Mean	Median	Minimum	Maximum	Std Dev
Units_Sold Revenue	4.0000000 1460.00	4.0000000 1500.00	2.0000000 1000.00	6.0000000 1800.00	1.5811388 357.7708764

proc freq data=bookstore_sales;

    tables Genre Branch;

run;

Output:

                                                                 The FREQ Procedure

Genre	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Fiction	3	60.00	3	60.00
Non-Fiction	1	20.00	4	80.00
Science	1	20.00	5	100.00

Branch	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Delhi	1	20.00	1	20.00
Hyderabad	2	40.00	3	60.00
Mumbai	2	40.00	5	100.00

proc sgplot data=bookstore_sales;

    vbar Genre / response=Revenue stat=sum;

    title "Total Revenue by Genre";

run;

Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           2.96 seconds

      cpu time            0.62 seconds

NOTE: Listing image output written to SGPlot1.png.

NOTE: There were 5 observations read from the data set WORK.BOOKSTORE_SALES.

proc sgplot data=bookstore_sales;

    series x=Date y=Revenue / markers;

    title "Daily Revenue Trend";

run;

Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           0.73 seconds

      cpu time            0.04 seconds

NOTE: The column format YYMMDD10 is replaced by an auto-generated format on the axis.

NOTE: The column format YYMMDD10 is replaced by an auto-generated format on the axis.

NOTE: Listing image output written to SGPlot3.png.

NOTE: There were 5 observations read from the data set WORK.BOOKSTORE_SALES.

data bookstore_sales;

    set bookstore_sales;

    Avg_Price = Revenue / Units_Sold;

run;

proc print;run;

Output:

Daily Revenue Trend

Obs	Date	Branch	Genre	Title	Author	Units_Sold	Revenue	Avg_Price
1	2025-04-01	Hyderabad	Fiction	The Silent Patient	Alex Michaelides	5	1500	300
2	2025-04-01	Mumbai	Non-Fiction	Sapiens	Yuval Noah Harari	3	1800	600
3	2025-04-01	Delhi	Science	A Brief History of Time	Stephen Hawking	2	1000	500
4	2025-04-02	Hyderabad	Fiction	The Midnight Library	Matt Haig	4	1200	300
5	2025-04-02	Mumbai	Fiction	The Alchemist	Paulo Coelho	6	1800	300

proc sort data=bookstore_sales;

    by descending Revenue;

run;

proc print;run;

Output:

Obs	Date	Branch	Genre	Title	Author	Units_Sold	Revenue	Avg_Price
1	2025-04-01	Mumbai	Non-Fiction	Sapiens	Yuval Noah Harari	3	1800	600
2	2025-04-02	Mumbai	Fiction	The Alchemist	Paulo Coelho	6	1800	300
3	2025-04-01	Hyderabad	Fiction	The Silent Patient	Alex Michaelides	5	1500	300
4	2025-04-02	Hyderabad	Fiction	The Midnight Library	Matt Haig	4	1200	300
5	2025-04-01	Delhi	Science	A Brief History of Time	Stephen Hawking	2	1000	500

proc report data=bookstore_sales nowd;

    column Branch Units_Sold Revenue;

    define Branch / group;

    define Units_Sold / analysis sum;

    define Revenue / analysis sum;

    title "Total Units Sold and Revenue by Branch";

run;

Output:

Total Units Sold and Revenue by Branch

Branch	Units_Sold	Revenue
Delhi	2	1000
Hyderabad	9	2700
Mumbai	9	3600

proc corr data=bookstore_sales;

    var Units_Sold Revenue;

run;

Output:

                                                                  The CORR Procedure

2 Variables:	Units_Sold Revenue

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Units_Sold	5	4.00000	1.58114	20.00000	2.00000	6.00000
Revenue	5	1460	357.77088	7300	1000	1800

Pearson Correlation Coefficients, N = 5 Prob > |r| under H0: Rho=0
	Units_Sold	Revenue
Units_Sold	1.00000	0.57452 0.3110
Revenue	0.57452 0.3110	1.00000

proc reg data=bookstore_sales;

    model Revenue = Units_Sold;

    title "Regression Analysis: Revenue vs. Units Sold";

run;

Output: 

Regression Analysis: Revenue vs. Units Sold

                                                                The REG Procedure

                                                                  Model: MODEL1

                                                            Dependent Variable: Revenue

Number of Observations Read	5
Number of Observations Used	5

Analysis of Variance
Source	DF	Sum of Squares	Mean Square	F Value	Pr > F
Model	1	169000	169000	1.48	0.3110
Error	3	343000	114333
Corrected Total	4	512000

Root MSE	338.13212	R-Square	0.3301
Dependent Mean	1460.00000	Adj R-Sq	0.1068
Coeff Var	23.15973

Parameter Estimates
Variable	DF	Parameter Estimate	Standard Error	t Value	Pr > |t|
Intercept	1	940.00000	453.65185	2.07	0.1300
Units_Sold	1	130.00000	106.92677	1.22	0.3110

proc export data=bookstore_sales

    outfile="bookstore_sales.csv"

    dbms=csv

    replace;

run;

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