193.Can SAS Help Us Choose the Most Efficient Cooler?

Can SAS Help Us Choose the Most Efficient Cooler?

 /*A dataset of coolers and analyze it using different SAS procedures*/

Step 1:Dataset consists of variables like

Cooler_ID: Unique identifier

Cooler_Type: Type of cooler

Brand: Cooler brand

Cooling_Capacity: In liters/hour

Power_Consumption: In Watts

Price: In INR

Rating: Customer rating (1 to 5 stars)

Warranty_Years: Years of warranty

Stock_Status: Availability in stock

Step 2: Creating the Dataset

data coolers;

  input Cooler_ID $ Cooler_Type: $15. Brand $ Cooling_Capacity Power_Consumption Price Rating Warranty_Years Stock_Status $;

  datalines;

C001 AirCooler Symphony 30 180 6500 4.2 1 Yes

C002 WaterCooler BlueStar 60 420 14500 4.7 2 Yes

C003 TowerCooler Orient 40 200 7200 4.1 1 Yes

C004 DesertCooler Bajaj 80 500 12500 4.5 2 No

C005 MiniCooler Cello 20 120 4800 3.8 1 Yes

C006 AirCooler Kenstar 35 210 6900 4.0 1 No

C007 WaterCooler Voltas 70 450 15000 4.6 3 Yes

C008 TowerCooler Havells 45 220 7800 4.0 1 Yes

C009 DesertCooler Symphony 85 550 13200 4.3 2 Yes

C010 MiniCooler Bajaj 15 100 4300 3.5 1 No

C011 AirCooler Crompton 32 190 6600 4.1 1 Yes

C012 WaterCooler LG 65 400 14800 4.8 2 Yes

C013 TowerCooler Kenstar 50 230 8000 4.2 1 Yes

C014 DesertCooler Orient 90 520 13500 4.4 2 No

C015 MiniCooler BlueStar 18 110 4600 3.7 1 Yes

;

run;

proc print;run;

Output:

Obs	Cooler_ID	Cooler_Type	Brand	Cooling_Capacity	Power_Consumption	Price	Rating	Warranty_Years	Stock_Status
1	C001	AirCooler	Symphony	30	180	6500	4.2	1	Yes
2	C002	WaterCooler	BlueStar	60	420	14500	4.7	2	Yes
3	C003	TowerCooler	Orient	40	200	7200	4.1	1	Yes
4	C004	DesertCooler	Bajaj	80	500	12500	4.5	2	No
5	C005	MiniCooler	Cello	20	120	4800	3.8	1	Yes
6	C006	AirCooler	Kenstar	35	210	6900	4.0	1	No
7	C007	WaterCooler	Voltas	70	450	15000	4.6	3	Yes
8	C008	TowerCooler	Havells	45	220	7800	4.0	1	Yes
9	C009	DesertCooler	Symphony	85	550	13200	4.3	2	Yes
10	C010	MiniCooler	Bajaj	15	100	4300	3.5	1	No
11	C011	AirCooler	Crompton	32	190	6600	4.1	1	Yes
12	C012	WaterCooler	LG	65	400	14800	4.8	2	Yes
13	C013	TowerCooler	Kenstar	50	230	8000	4.2	1	Yes
14	C014	DesertCooler	Orient	90	520	13500	4.4	2	No
15	C015	MiniCooler	BlueStar	18	110	4600	3.7	1	Yes

Step 3: Exploring the Dataset

PROC PRINT: Basic View

proc print data=coolers;

  title "Full Cooler Dataset";

run;

Output:

Full Cooler Dataset

Obs	Cooler_ID	Cooler_Type	Brand	Cooling_Capacity	Power_Consumption	Price	Rating	Warranty_Years	Stock_Status
1	C001	AirCooler	Symphony	30	180	6500	4.2	1	Yes
2	C002	WaterCooler	BlueStar	60	420	14500	4.7	2	Yes
3	C003	TowerCooler	Orient	40	200	7200	4.1	1	Yes
4	C004	DesertCooler	Bajaj	80	500	12500	4.5	2	No
5	C005	MiniCooler	Cello	20	120	4800	3.8	1	Yes
6	C006	AirCooler	Kenstar	35	210	6900	4.0	1	No
7	C007	WaterCooler	Voltas	70	450	15000	4.6	3	Yes
8	C008	TowerCooler	Havells	45	220	7800	4.0	1	Yes
9	C009	DesertCooler	Symphony	85	550	13200	4.3	2	Yes
10	C010	MiniCooler	Bajaj	15	100	4300	3.5	1	No
11	C011	AirCooler	Crompton	32	190	6600	4.1	1	Yes
12	C012	WaterCooler	LG	65	400	14800	4.8	2	Yes
13	C013	TowerCooler	Kenstar	50	230	8000	4.2	1	Yes
14	C014	DesertCooler	Orient	90	520	13500	4.4	2	No
15	C015	MiniCooler	BlueStar	18	110	4600	3.7	1	Yes

PROC CONTENTS: Metadata of Dataset

proc contents data=coolers;

  title "Cooler Dataset Structure";

run;

Output:

Cooler Dataset Structure

The CONTENTS Procedure

Data Set Name	WORK.COOLERS	Observations	15
Member Type	DATA	Variables	9
Engine	V9	Indexes	0
Created	14/09/2015 00:34:33	Observation Length	72
Last Modified	14/09/2015 00:34:33	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	908
Obs in First Data Page	15
Number of Data Set Repairs	0
ExtendObsCounter	YES
Filename	C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD5308_DESKTOP-QFAA4KV_\coolers.sas7bdat
Release Created	9.0401M2
Host Created	X64_8HOME

Alphabetic List of Variables and Attributes
#	Variable	Type	Len
3	Brand	Char	8
1	Cooler_ID	Char	8
2	Cooler_Type	Char	8
4	Cooling_Capacity	Num	8
5	Power_Consumption	Num	8
6	Price	Num	8
7	Rating	Num	8
9	Stock_Status	Char	8
8	Warranty_Years	Num	8

Step 4: Descriptive Statistics

PROC MEANS: Numeric Summary

proc means data=coolers n mean min max std;

  var Cooling_Capacity Power_Consumption Price Rating;

  title "Descriptive Statistics for Numeric Variables";

run;

Output:

Descriptive Statistics for Numeric Variables

The MEANS Procedure

Variable	N	Mean	Minimum	Maximum	Std Dev
Cooling_Capacity Power_Consumption Price Rating	15 15 15 15	49.0000000 293.3333333 9346.67 4.1933333	15.0000000 100.0000000 4300.00 3.5000000	90.0000000 550.0000000 15000.00 4.8000000	24.8682241 160.6978828 4048.08 0.3692979

PROC UNIVARIATE: Detailed Stats

proc univariate data=coolers;

  var Price;

  title "Distribution of Cooler Prices";

run;

Output:

Distribution of Cooler Prices

The UNIVARIATE Procedure

Variable: Price

Moments
N	15	Sum Weights	15
Mean	9346.66667	Sum Observations	140200
Std Deviation	4048.08009	Variance	16386952.4
Skewness	0.2932028	Kurtosis	-1.7229701
Uncorrected SS	1539820000	Corrected SS	229417333
Coeff Variation	43.3104146	Std Error Mean	1045.20978

Basic Statistical Measures
Location		Variability
Mean	9346.667	Std Deviation	4048
Median	7800.000	Variance	16386952
Mode	.	Range	10700
		Interquartile Range	7000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	8.942383	Pr > |t|	<.0001
Sign	M	7.5	Pr >= |M|	<.0001
Signed Rank	S	60	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	15000
99%	15000
95%	15000
90%	14800
75% Q3	13500
50% Median	7800
25% Q1	6500
10%	4600
5%	4300
1%	4300
0% Min	4300

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
4300	10	13200	9
4600	15	13500	14
4800	5	14500	2
6500	1	14800	12
6600	11	15000	7

Step 5: Frequency Analysis

PROC FREQ: Cooler Types and Brands

proc freq data=coolers;

  tables Cooler_Type Brand Stock_Status;

  title "Frequency Tables for Categorical Variables";

run;

Output:

Frequency Tables for Categorical Variables

The FREQ Procedure

Cooler_Type	Frequency	Percent	Cumulative Frequency	Cumulative Percent
AirCooler	3	20.00	3	20.00
DesertCooler	3	20.00	6	40.00
MiniCooler	3	20.00	9	60.00
TowerCooler	3	20.00	12	80.00
WaterCooler	3	20.00	15	100.00

Brand	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Bajaj	2	13.33	2	13.33
BlueStar	2	13.33	4	26.67
Cello	1	6.67	5	33.33
Crompton	1	6.67	6	40.00
Havells	1	6.67	7	46.67
Kenstar	2	13.33	9	60.00
LG	1	6.67	10	66.67
Orient	2	13.33	12	80.00
Symphony	2	13.33	14	93.33
Voltas	1	6.67	15	100.00

Stock_Status	Frequency	Percent	Cumulative Frequency	Cumulative Percent
No	4	26.67	4	26.67
Yes	11	73.33	15	100.00

Step 6: Custom Reports

PROC REPORT: Summarize by Cooler Type

proc report data=coolers nowd;

  column Cooler_Type Cooling_Capacity Price;

  define Cooler_Type / group;

  define Cooling_Capacity / analysis mean;

  define Price / analysis mean;

  title "Average Cooling Capacity and Price by Cooler Type";

run;

Output:

Average Cooling Capacity and Price by Cooler Type

Cooler_Type	Cooling_Capacity	Price
AirCooler	32.333333	6666.6667
DesertCooler	85	13066.667
MiniCooler	17.666667	4566.6667
TowerCooler	45	7666.6667
WaterCooler	65	14766.667

Step 7: Sorting and Filtering

PROC SORT: By Price Descending

proc sort data=coolers out=sorted_price;

  by descending Price;

run;

proc print data=sorted_price;

  title "Coolers Sorted by Price (Descending)";

run;

Output:

Coolers Sorted by Price (Descending)

Obs	Cooler_ID	Cooler_Type	Brand	Cooling_Capacity	Power_Consumption	Price	Rating	Warranty_Years	Stock_Status
1	C007	WaterCooler	Voltas	70	450	15000	4.6	3	Yes
2	C012	WaterCooler	LG	65	400	14800	4.8	2	Yes
3	C002	WaterCooler	BlueStar	60	420	14500	4.7	2	Yes
4	C014	DesertCooler	Orient	90	520	13500	4.4	2	No
5	C009	DesertCooler	Symphony	85	550	13200	4.3	2	Yes
6	C004	DesertCooler	Bajaj	80	500	12500	4.5	2	No
7	C013	TowerCooler	Kenstar	50	230	8000	4.2	1	Yes
8	C008	TowerCooler	Havells	45	220	7800	4.0	1	Yes
9	C003	TowerCooler	Orient	40	200	7200	4.1	1	Yes
10	C006	AirCooler	Kenstar	35	210	6900	4.0	1	No
11	C011	AirCooler	Crompton	32	190	6600	4.1	1	Yes
12	C001	AirCooler	Symphony	30	180	6500	4.2	1	Yes
13	C005	MiniCooler	Cello	20	120	4800	3.8	1	Yes
14	C015	MiniCooler	BlueStar	18	110	4600	3.7	1	Yes
15	C010	MiniCooler	Bajaj	15	100	4300	3.5	1	No

PROC SQL: Top 5 Expensive Coolers

proc sql outobs=5;

  create table top_expensive as

  select * from coolers

  order by Price desc;

quit;

proc print data=top_expensive;

  title "Top 5 Most Expensive Coolers";

run;

Output:

Top 5 Most Expensive Coolers

Obs	Cooler_ID	Cooler_Type	Brand	Cooling_Capacity	Power_Consumption	Price	Rating	Warranty_Years	Stock_Status
1	C007	WaterCooler	Voltas	70	450	15000	4.6	3	Yes
2	C012	WaterCooler	LG	65	400	14800	4.8	2	Yes
3	C002	WaterCooler	BlueStar	60	420	14500	4.7	2	Yes
4	C014	DesertCooler	Orient	90	520	13500	4.4	2	No
5	C009	DesertCooler	Symphony	85	550	13200	4.3	2	Yes

Step 8: Advanced SQL Analysis

SQL: Average Price and Power by Cooler Type

proc sql;

  select Cooler_Type,

         avg(Price) as Avg_Price,

         avg(Power_Consumption) as Avg_Power

  from coolers

  group by Cooler_Type;

quit;

Output:

Cooler_Type	Avg_Price	Avg_Power
AirCooler	6666.667	193.3333
DesertCooler	13066.67	523.3333
MiniCooler	4566.667	110
TowerCooler	7666.667	216.6667
WaterCooler	14766.67	423.3333

SQL: Count of In-Stock Coolers by Brand

proc sql;

  select Brand, count(*) as InStock_Count

  from coolers

  where Stock_Status = 'Yes'

  group by Brand;

quit;

Output:

Brand	InStock_Count
BlueStar	2
Cello	1
Crompton	1
Havells	1
Kenstar	1
LG	1
Orient	1
Symphony	2
Voltas	1

Step 9: Creating a Macro to Filter Coolers

Macro: Coolers by Rating and Type

%macro filter_coolers(rating=, type=);

  proc sql;

    select * from coolers

    where Rating >= &rating and Cooler_Type = "&type";

  quit;

%mend;

%filter_coolers(rating=4.0, type=AirCooler)

Output:

Cooler_ID	Cooler_Type	Brand	Cooling_Capacity	Power_Consumption	Price	Rating	Warranty_Years	Stock_Status
C001	AirCooler	Symphony	30	180	6500	4.2	1	Yes
C006	AirCooler	Kenstar	35	210	6900	4	1	No
C011	AirCooler	Crompton	32	190	6600	4.1	1	Yes

Step 10: Creating Macro for Summary Reports

Macro: Summarize Any Numeric Variable

%macro summary(var=);

  proc means data=coolers n mean min max std;

    var &var;

    title "Summary Statistics for &var";

  run;

%mend;

%summary(var=Power_Consumption)

Output:

Summary Statistics for Power_Consumption

The MEANS Procedure

Analysis Variable : Power_Consumption
N	Mean	Minimum	Maximum	Std Dev
15	293.3333333	100.0000000	550.0000000	160.6978828

%summary(var=Price)

Output:

Summary Statistics for Price

The MEANS Procedure

Analysis Variable : Price
N	Mean	Minimum	Maximum	Std Dev
15	9346.67	4300.00	15000.00	4048.08

Step 11: Visualization with PROC SGPLOT

proc sgplot data=coolers;

  vbar Cooler_Type / response=Price stat=mean;

  title "Average Price by Cooler Type";

run;

Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           3.65 seconds

      cpu time            0.62 seconds

NOTE: Listing image output written to SGPlot1.png.

NOTE: There were 15 observations read from the data set WORK.COOLERS.

proc sgplot data=coolers;

  scatter x=Cooling_Capacity y=Price / group=Cooler_Type;

  title "Cooling Capacity vs Price";

run;

Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           0.78 seconds

      cpu time            0.03 seconds

NOTE: Listing image output written to SGPlot3.png.

NOTE: There were 15 observations read from the data set WORK.COOLERS.

Step 12: Creating Derived Variables

New Variable: Efficiency Score

data coolers_updated;

  set coolers;

  Efficiency = Cooling_Capacity / Power_Consumption;

run;

proc print data=coolers_updated;

  title "Coolers with Efficiency Score";

run;

Output:

Coolers with Efficiency Score

Obs	Cooler_ID	Cooler_Type	Brand	Cooling_Capacity	Power_Consumption	Price	Rating	Warranty_Years	Stock_Status	Efficiency
1	C001	AirCooler	Symphony	30	180	6500	4.2	1	Yes	0.16667
2	C002	WaterCooler	BlueStar	60	420	14500	4.7	2	Yes	0.14286
3	C003	TowerCooler	Orient	40	200	7200	4.1	1	Yes	0.20000
4	C004	DesertCooler	Bajaj	80	500	12500	4.5	2	No	0.16000
5	C005	MiniCooler	Cello	20	120	4800	3.8	1	Yes	0.16667
6	C006	AirCooler	Kenstar	35	210	6900	4.0	1	No	0.16667
7	C007	WaterCooler	Voltas	70	450	15000	4.6	3	Yes	0.15556
8	C008	TowerCooler	Havells	45	220	7800	4.0	1	Yes	0.20455
9	C009	DesertCooler	Symphony	85	550	13200	4.3	2	Yes	0.15455
10	C010	MiniCooler	Bajaj	15	100	4300	3.5	1	No	0.15000
11	C011	AirCooler	Crompton	32	190	6600	4.1	1	Yes	0.16842
12	C012	WaterCooler	LG	65	400	14800	4.8	2	Yes	0.16250
13	C013	TowerCooler	Kenstar	50	230	8000	4.2	1	Yes	0.21739
14	C014	DesertCooler	Orient	90	520	13500	4.4	2	No	0.17308
15	C015	MiniCooler	BlueStar	18	110	4600	3.7	1	Yes	0.16364

Step 13: Ranking Coolers by Rating

proc rank data=coolers out=ranked_coolers descending ties=low;

  var Rating;

  ranks Rank_Rating;

run;

proc print data=ranked_coolers;

  title "Coolers Ranked by Rating";

run;

Output:

Coolers Ranked by Rating

Obs	Cooler_ID	Cooler_Type	Brand	Cooling_Capacity	Power_Consumption	Price	Rating	Warranty_Years	Stock_Status	Rank_Rating
1	C001	AirCooler	Symphony	30	180	6500	4.2	1	Yes	7
2	C002	WaterCooler	BlueStar	60	420	14500	4.7	2	Yes	2
3	C003	TowerCooler	Orient	40	200	7200	4.1	1	Yes	9
4	C004	DesertCooler	Bajaj	80	500	12500	4.5	2	No	4
5	C005	MiniCooler	Cello	20	120	4800	3.8	1	Yes	13
6	C006	AirCooler	Kenstar	35	210	6900	4.0	1	No	11
7	C007	WaterCooler	Voltas	70	450	15000	4.6	3	Yes	3
8	C008	TowerCooler	Havells	45	220	7800	4.0	1	Yes	11
9	C009	DesertCooler	Symphony	85	550	13200	4.3	2	Yes	6
10	C010	MiniCooler	Bajaj	15	100	4300	3.5	1	No	15
11	C011	AirCooler	Crompton	32	190	6600	4.1	1	Yes	9
12	C012	WaterCooler	LG	65	400	14800	4.8	2	Yes	1
13	C013	TowerCooler	Kenstar	50	230	8000	4.2	1	Yes	7
14	C014	DesertCooler	Orient	90	520	13500	4.4	2	No	5
15	C015	MiniCooler	BlueStar	18	110	4600	3.7	1	Yes	14

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