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

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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;


PRACTICE AND COMMENT YOUR CODE: 

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