347.FOREST TYPES DATA ANALYSIS USING SAS DATA STEP | PROC SQL | PROC MEANS | PROC FORMAT | PROC UNIVARIATE | MACROS | DATE FUNCTIONS (INTNX, INTCK) | MERGE | APPEND

FOREST TYPES DATA ANALYSIS USING SAS DATA STEP | PROC SQL | PROC MEANS | PROC FORMAT |  PROC UNIVARIATE | MACROS | DATE FUNCTIONS (INTNX, INTCK) | MERGE | APPEND

options nocenter;

1.FOREST BASE CREATING THE BASE DATASET

data forest_base;

    length Forest_Name $20 Country $15 Fire_Risk $10;

    format Survey_Date date9.;

    input Forest_Name $ Area Country $ Animal_Count Rainfall Fire_Risk $ 

          Survey_Date :date9.;

    datalines;

Amazon 5500000 Brazil 3000000 2200 High 01JAN2023

Congo 3700000 Congo 1800000 1900 Medium 15FEB2023

Sundarbans 10000 India 250000 1800 High 20MAR2023

BlackForest 6000 Germany 150000 1200 Low 05APR2023

Taiga 8000000 Russia 4000000 600 Medium 10MAY2023

Daintree 1200 Australia 90000 3500 Medium 12JUN2023

Borneo 740000 Malaysia 600000 3000 High 18JUL2023

Kinabalu 750 Malaysia 120000 2800 Medium 25AUG2023

Sinharaja 88 SriLanka 65000 5000 Low 01SEP2023

Yakushima 505 Japan 70000 4300 Low 10OCT2023

Tongass 68000 USA 200000 4000 Medium 15NOV2023

Sherwood 423 UK 35000 900 Low 01DEC2023

;

run;

proc print data=forest_base;

run;

OUTPUT:

Obs	Forest_Name	Country	Fire_Risk	Survey_Date	Area	Animal_Count	Rainfall
1	Amazon	Brazil	High	01JAN2023	5500000	3000000	2200
2	Congo	Congo	Medium	15FEB2023	3700000	1800000	1900
3	Sundarbans	India	High	20MAR2023	10000	250000	1800
4	BlackForest	Germany	Low	05APR2023	6000	150000	1200
5	Taiga	Russia	Medium	10MAY2023	8000000	4000000	600
6	Daintree	Australia	Medium	12JUN2023	1200	90000	3500
7	Borneo	Malaysia	High	18JUL2023	740000	600000	3000
8	Kinabalu	Malaysia	Medium	25AUG2023	750	120000	2800
9	Sinharaja	SriLanka	Low	01SEP2023	88	65000	5000
10	Yakushima	Japan	Low	10OCT2023	505	70000	4300
11	Tongass	USA	Medium	15NOV2023	68000	200000	4000
12	Sherwood	UK	Low	01DEC2023	423	35000	900

2.USING PROC CONTENTS (STRUCTURAL CHECK)

proc contents data=forest_base;

run;

OUTPUT:

The CONTENTS Procedure

Data Set Name	WORK.FOREST_BASE	Observations	12
Member Type	DATA	Variables	7
Engine	V9	Indexes	0
Created	12/23/2025 07:19:56	Observation Length	80
Last Modified	12/23/2025 07:19:56	Deleted Observations	0
Protection		Compressed	NO
Data Set Type		Sorted	NO
Label
Data Representation	SOLARIS_X86_64, LINUX_X86_64, ALPHA_TRU64, LINUX_IA64
Encoding	utf-8 Unicode (UTF-8)

Engine/Host Dependent Information
Data Set Page Size	131072
Number of Data Set Pages	1
First Data Page	1
Max Obs per Page	1635
Obs in First Data Page	12
Number of Data Set Repairs	0
Filename	/saswork/SAS_workD61200018CFF_odaws01-apse1-2.oda.sas.com/SAS_work11FC00018CFF_odaws01-apse1-2.oda.sas.com/forest_base.sas7bdat
Release Created	9.0401M8
Host Created	Linux
Inode Number	134334086
Access Permission	rw-r--r--
Owner Name	u63247146
File Size	256KB
File Size (bytes)	262144

Alphabetic List of Variables and Attributes
#	Variable	Type	Len	Format
6	Animal_Count	Num	8
5	Area	Num	8
2	Country	Char	15
3	Fire_Risk	Char	10
1	Forest_Name	Char	20
7	Rainfall	Num	8
4	Survey_Date	Num	8	DATE9.

3.PROC SQL FOR DERIVED DATA

Creating an Enhanced Dataset with Safety Index

proc sql;

    create table forest_sql as

    select *,

           case 

              when Rainfall < 1000 and Animal_Count < 100000 then 'HIGH RISK'

              when Rainfall between 1000 and 2500 then 'MODERATE RISK'

              else 'LOW RISK'

           end as Safety_Level

    from forest_base;

quit;

proc print data=forest_sql;

run;

OUTPUT:

Obs	Forest_Name	Country	Fire_Risk	Survey_Date	Area	Animal_Count	Rainfall	Safety_Level
1	Amazon	Brazil	High	01JAN2023	5500000	3000000	2200	MODERATE RISK
2	Congo	Congo	Medium	15FEB2023	3700000	1800000	1900	MODERATE RISK
3	Sundarbans	India	High	20MAR2023	10000	250000	1800	MODERATE RISK
4	BlackForest	Germany	Low	05APR2023	6000	150000	1200	MODERATE RISK
5	Taiga	Russia	Medium	10MAY2023	8000000	4000000	600	LOW RISK
6	Daintree	Australia	Medium	12JUN2023	1200	90000	3500	LOW RISK
7	Borneo	Malaysia	High	18JUL2023	740000	600000	3000	LOW RISK
8	Kinabalu	Malaysia	Medium	25AUG2023	750	120000	2800	LOW RISK
9	Sinharaja	SriLanka	Low	01SEP2023	88	65000	5000	LOW RISK
10	Yakushima	Japan	Low	10OCT2023	505	70000	4300	LOW RISK
11	Tongass	USA	Medium	15NOV2023	68000	200000	4000	LOW RISK
12	Sherwood	UK	Low	01DEC2023	423	35000	900	HIGH RISK

4.DATE FUNCTIONS (INTNX & INTCK)

Adding Next Survey Date

data forest_dates;

    set forest_sql;

    Next_Survey = intnx('month', Survey_Date, 6, 'same');

    Months_Since = intck('month', Survey_Date, today());

    format Next_Survey date9.;

run;

proc print data=forest_dates;

run;

OUTPUT:

Obs	Forest_Name	Country	Fire_Risk	Survey_Date	Area	Animal_Count	Rainfall	Safety_Level	Next_Survey	Months_Since
1	Amazon	Brazil	High	01JAN2023	5500000	3000000	2200	MODERATE RISK	01JUL2023	35
2	Congo	Congo	Medium	15FEB2023	3700000	1800000	1900	MODERATE RISK	15AUG2023	34
3	Sundarbans	India	High	20MAR2023	10000	250000	1800	MODERATE RISK	20SEP2023	33
4	BlackForest	Germany	Low	05APR2023	6000	150000	1200	MODERATE RISK	05OCT2023	32
5	Taiga	Russia	Medium	10MAY2023	8000000	4000000	600	LOW RISK	10NOV2023	31
6	Daintree	Australia	Medium	12JUN2023	1200	90000	3500	LOW RISK	12DEC2023	30
7	Borneo	Malaysia	High	18JUL2023	740000	600000	3000	LOW RISK	18JAN2024	29
8	Kinabalu	Malaysia	Medium	25AUG2023	750	120000	2800	LOW RISK	25FEB2024	28
9	Sinharaja	SriLanka	Low	01SEP2023	88	65000	5000	LOW RISK	01MAR2024	27
10	Yakushima	Japan	Low	10OCT2023	505	70000	4300	LOW RISK	10APR2024	26
11	Tongass	USA	Medium	15NOV2023	68000	200000	4000	LOW RISK	15MAY2024	25
12	Sherwood	UK	Low	01DEC2023	423	35000	900	HIGH RISK	01JUN2024	24

5.PROC MEANS (NUMERICAL SUMMARY)

proc means data=forest_dates min max mean;

    var Area Animal_Count Rainfall Months_Since;

run;

OUTPUT:

The MEANS Procedure

Variable	Minimum	Maximum	Mean
Area Animal_Count Rainfall Months_Since	88.0000000 35000.00 600.0000000 24.0000000	8000000.00 4000000.00 5000.00 35.0000000	1502247.17 865000.00 2600.00 29.5000000

6.PROC FORMAT (CUSTOM RISK LABELS)

proc format;

    value rainfmt

        low - 1000 = 'Low Rainfall'

        1001 - 2500 = 'Moderate Rainfall'

        2501 - high = 'Heavy Rainfall';

run;

LOG:

NOTE: Format RAINFMT has been output.

proc print data=forest_dates;

    format Rainfall rainfmt.;

run;

OUTPUT:

Obs	Forest_Name	Country	Fire_Risk	Survey_Date	Area	Animal_Count	Rainfall	Safety_Level	Next_Survey	Months_Since
1	Amazon	Brazil	High	01JAN2023	5500000	3000000	Moderate Rainfall	MODERATE RISK	01JUL2023	35
2	Congo	Congo	Medium	15FEB2023	3700000	1800000	Moderate Rainfall	MODERATE RISK	15AUG2023	34
3	Sundarbans	India	High	20MAR2023	10000	250000	Moderate Rainfall	MODERATE RISK	20SEP2023	33
4	BlackForest	Germany	Low	05APR2023	6000	150000	Moderate Rainfall	MODERATE RISK	05OCT2023	32
5	Taiga	Russia	Medium	10MAY2023	8000000	4000000	Low Rainfall	LOW RISK	10NOV2023	31
6	Daintree	Australia	Medium	12JUN2023	1200	90000	Heavy Rainfall	LOW RISK	12DEC2023	30
7	Borneo	Malaysia	High	18JUL2023	740000	600000	Heavy Rainfall	LOW RISK	18JAN2024	29
8	Kinabalu	Malaysia	Medium	25AUG2023	750	120000	Heavy Rainfall	LOW RISK	25FEB2024	28
9	Sinharaja	SriLanka	Low	01SEP2023	88	65000	Heavy Rainfall	LOW RISK	01MAR2024	27
10	Yakushima	Japan	Low	10OCT2023	505	70000	Heavy Rainfall	LOW RISK	10APR2024	26
11	Tongass	USA	Medium	15NOV2023	68000	200000	Heavy Rainfall	LOW RISK	15MAY2024	25
12	Sherwood	UK	Low	01DEC2023	423	35000	Low Rainfall	HIGH RISK	01JUN2024	24

7.PROC UNIVARIATE (DISTRIBUTION ANALYSIS)

proc univariate data=forest_dates;

    var Rainfall Animal_Count;

    histogram Rainfall Animal_Count;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Rainfall

Moments
N	12	Sum Weights	12
Mean	2600	Sum Observations	31200
Std Deviation	1406.47852	Variance	1978181.82
Skewness	0.22372835	Kurtosis	-1.0163311
Uncorrected SS	102880000	Corrected SS	21760000
Coeff Variation	54.0953276	Std Error Mean	406.015375

Basic Statistical Measures
Location		Variability
Mean	2600.000	Std Deviation	1406
Median	2500.000	Variance	1978182
Mode	.	Range	4400
		Interquartile Range	2250

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	6.403698	Pr > |t|	<.0001
Sign	M	6	Pr >= |M|	0.0005
Signed Rank	S	39	Pr >= |S|	0.0005

Quantiles (Definition 5)
Level	Quantile
100% Max	5000
99%	5000
95%	5000
90%	4300
75% Q3	3750
50% Median	2500
25% Q1	1500
10%	900
5%	600
1%	600
0% Min	600

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
600	5	3000	7
900	12	3500	6
1200	4	4000	11
1800	3	4300	10
1900	2	5000	9

The UNIVARIATE Procedure

The UNIVARIATE Procedure

Variable: Animal_Count

Moments
N	12	Sum Weights	12
Mean	865000	Sum Observations	10380000
Std Deviation	1340934.21	Variance	1.7981E12
Skewness	1.71859782	Kurtosis	1.84889294
Uncorrected SS	2.87579E13	Corrected SS	1.97792E13
Coeff Variation	155.021296	Std Error Mean	387094.362

Basic Statistical Measures
Location		Variability
Mean	865000.0	Std Deviation	1340934
Median	175000.0	Variance	1.7981E12
Mode	.	Range	3965000
		Interquartile Range	1120000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	2.234597	Pr > |t|	0.0471
Sign	M	6	Pr >= |M|	0.0005
Signed Rank	S	39	Pr >= |S|	0.0005

Quantiles (Definition 5)
Level	Quantile
100% Max	4000000
99%	4000000
95%	4000000
90%	3000000
75% Q3	1200000
50% Median	175000
25% Q1	80000
10%	65000
5%	35000
1%	35000
0% Min	35000

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
35000	12	250000	3
65000	9	600000	7
70000	10	1800000	2
90000	6	3000000	1
120000	8	4000000	5

The UNIVARIATE Procedure

8.MACRO FOR FOREST SAFETY CLASSIFICATION

%macro safety_flag(ds=, out=);

    data &out;

        set &ds;

        if Rainfall < 1000 and Fire_Risk='High' then Safety_Flag='CRITICAL';

        else if Fire_Risk='Medium' then Safety_Flag='WATCH';

        else Safety_Flag='SAFE';

    run;

proc print data=&out;

    run;

%mend;

%safety_flag(ds=forest_dates, out=forest_safe);

OUTPUT:

Obs	Forest_Name	Country	Fire_Risk	Survey_Date	Area	Animal_Count	Rainfall	Safety_Level	Next_Survey	Months_Since	Safety_Flag
1	Amazon	Brazil	High	01JAN2023	5500000	3000000	2200	MODERATE RISK	01JUL2023	35	SAFE
2	Congo	Congo	Medium	15FEB2023	3700000	1800000	1900	MODERATE RISK	15AUG2023	34	WATCH
3	Sundarbans	India	High	20MAR2023	10000	250000	1800	MODERATE RISK	20SEP2023	33	SAFE
4	BlackForest	Germany	Low	05APR2023	6000	150000	1200	MODERATE RISK	05OCT2023	32	SAFE
5	Taiga	Russia	Medium	10MAY2023	8000000	4000000	600	LOW RISK	10NOV2023	31	WATCH
6	Daintree	Australia	Medium	12JUN2023	1200	90000	3500	LOW RISK	12DEC2023	30	WATCH
7	Borneo	Malaysia	High	18JUL2023	740000	600000	3000	LOW RISK	18JAN2024	29	SAFE
8	Kinabalu	Malaysia	Medium	25AUG2023	750	120000	2800	LOW RISK	25FEB2024	28	WATCH
9	Sinharaja	SriLanka	Low	01SEP2023	88	65000	5000	LOW RISK	01MAR2024	27	SAFE
10	Yakushima	Japan	Low	10OCT2023	505	70000	4300	LOW RISK	10APR2024	26	SAFE
11	Tongass	USA	Medium	15NOV2023	68000	200000	4000	LOW RISK	15MAY2024	25	WATCH
12	Sherwood	UK	Low	01DEC2023	423	35000	900	HIGH RISK	01JUN2024	24	SAFE

9.USING MERGE STATEMENT

data animal_density;

    set forest_safe;

    Density = Animal_Count / Area;

    keep Forest_Name Density;

run;

proc print data=animal_density;

run;

OUTPUT:

Obs	Forest_Name	Density
1	Amazon	0.545
2	Congo	0.486
3	Sundarbans	25.000
4	BlackForest	25.000
5	Taiga	0.500
6	Daintree	75.000
7	Borneo	0.811
8	Kinabalu	160.000
9	Sinharaja	738.636
10	Yakushima	138.614
11	Tongass	2.941
12	Sherwood	82.742

proc sort data=forest_safe; by Forest_Name; run;

proc print data=forest_safe;

run;

OUTPUT:

Obs	Forest_Name	Country	Fire_Risk	Survey_Date	Area	Animal_Count	Rainfall	Safety_Level	Next_Survey	Months_Since	Safety_Flag
1	Amazon	Brazil	High	01JAN2023	5500000	3000000	2200	MODERATE RISK	01JUL2023	35	SAFE
2	BlackForest	Germany	Low	05APR2023	6000	150000	1200	MODERATE RISK	05OCT2023	32	SAFE
3	Borneo	Malaysia	High	18JUL2023	740000	600000	3000	LOW RISK	18JAN2024	29	SAFE
4	Congo	Congo	Medium	15FEB2023	3700000	1800000	1900	MODERATE RISK	15AUG2023	34	WATCH
5	Daintree	Australia	Medium	12JUN2023	1200	90000	3500	LOW RISK	12DEC2023	30	WATCH
6	Kinabalu	Malaysia	Medium	25AUG2023	750	120000	2800	LOW RISK	25FEB2024	28	WATCH
7	Sherwood	UK	Low	01DEC2023	423	35000	900	HIGH RISK	01JUN2024	24	SAFE
8	Sinharaja	SriLanka	Low	01SEP2023	88	65000	5000	LOW RISK	01MAR2024	27	SAFE
9	Sundarbans	India	High	20MAR2023	10000	250000	1800	MODERATE RISK	20SEP2023	33	SAFE
10	Taiga	Russia	Medium	10MAY2023	8000000	4000000	600	LOW RISK	10NOV2023	31	WATCH
11	Tongass	USA	Medium	15NOV2023	68000	200000	4000	LOW RISK	15MAY2024	25	WATCH
12	Yakushima	Japan	Low	10OCT2023	505	70000	4300	LOW RISK	10APR2024	26	SAFE

proc sort data=animal_density; by Forest_Name; run;

proc print data=animal_density;

run;

OUTPUT:

Obs	Forest_Name	Density
1	Amazon	0.545
2	BlackForest	25.000
3	Borneo	0.811
4	Congo	0.486
5	Daintree	75.000
6	Kinabalu	160.000
7	Sherwood	82.742
8	Sinharaja	738.636
9	Sundarbans	25.000
10	Taiga	0.500
11	Tongass	2.941
12	Yakushima	138.614

data forest_merged;

    merge forest_safe animal_density;

    by Forest_Name;

run;

proc print data=forest_merged;

run;

OUTPUT:

Obs	Forest_Name	Country	Fire_Risk	Survey_Date	Area	Animal_Count	Rainfall	Safety_Level	Next_Survey	Months_Since	Safety_Flag	Density
1	Amazon	Brazil	High	01JAN2023	5500000	3000000	2200	MODERATE RISK	01JUL2023	35	SAFE	0.545
2	BlackForest	Germany	Low	05APR2023	6000	150000	1200	MODERATE RISK	05OCT2023	32	SAFE	25.000
3	Borneo	Malaysia	High	18JUL2023	740000	600000	3000	LOW RISK	18JAN2024	29	SAFE	0.811
4	Congo	Congo	Medium	15FEB2023	3700000	1800000	1900	MODERATE RISK	15AUG2023	34	WATCH	0.486
5	Daintree	Australia	Medium	12JUN2023	1200	90000	3500	LOW RISK	12DEC2023	30	WATCH	75.000
6	Kinabalu	Malaysia	Medium	25AUG2023	750	120000	2800	LOW RISK	25FEB2024	28	WATCH	160.000
7	Sherwood	UK	Low	01DEC2023	423	35000	900	HIGH RISK	01JUN2024	24	SAFE	82.742
8	Sinharaja	SriLanka	Low	01SEP2023	88	65000	5000	LOW RISK	01MAR2024	27	SAFE	738.636
9	Sundarbans	India	High	20MAR2023	10000	250000	1800	MODERATE RISK	20SEP2023	33	SAFE	25.000
10	Taiga	Russia	Medium	10MAY2023	8000000	4000000	600	LOW RISK	10NOV2023	31	WATCH	0.500
11	Tongass	USA	Medium	15NOV2023	68000	200000	4000	LOW RISK	15MAY2024	25	WATCH	2.941
12	Yakushima	Japan	Low	10OCT2023	505	70000	4300	LOW RISK	10APR2024	26	SAFE	138.614

10.APPEND STATEMENT (ADDING NEW OBSERVATIONS)

Creating New Forest Data

data new_forests;

    length Forest_Name $20 Country $15 Fire_Risk $10;

    format Survey_Date date9.;

    input Forest_Name $ Area Country $ Animal_Count Rainfall Fire_Risk $ 

          Survey_Date :date9.;

    datalines;

GirForest 1412 India 40000 800 Medium 15JAN2024

;

run;

proc print data=new_forests;

run;

OUTPUT:

Obs	Forest_Name	Country	Fire_Risk	Survey_Date	Area	Animal_Count	Rainfall
1	GirForest	India	Medium	15JAN2024	1412	40000	800

Appending

proc append base=forest_merged 

            data=new_forests force;

run;

proc print data=forest_merged;

run;

OUTPUT:

Obs	Forest_Name	Country	Fire_Risk	Survey_Date	Area	Animal_Count	Rainfall	Safety_Level	Next_Survey	Months_Since	Safety_Flag	Density
1	Amazon	Brazil	High	01JAN2023	5500000	3000000	2200	MODERATE RISK	01JUL2023	35	SAFE	0.545
2	BlackForest	Germany	Low	05APR2023	6000	150000	1200	MODERATE RISK	05OCT2023	32	SAFE	25.000
3	Borneo	Malaysia	High	18JUL2023	740000	600000	3000	LOW RISK	18JAN2024	29	SAFE	0.811
4	Congo	Congo	Medium	15FEB2023	3700000	1800000	1900	MODERATE RISK	15AUG2023	34	WATCH	0.486
5	Daintree	Australia	Medium	12JUN2023	1200	90000	3500	LOW RISK	12DEC2023	30	WATCH	75.000
6	Kinabalu	Malaysia	Medium	25AUG2023	750	120000	2800	LOW RISK	25FEB2024	28	WATCH	160.000
7	Sherwood	UK	Low	01DEC2023	423	35000	900	HIGH RISK	01JUN2024	24	SAFE	82.742
8	Sinharaja	SriLanka	Low	01SEP2023	88	65000	5000	LOW RISK	01MAR2024	27	SAFE	738.636
9	Sundarbans	India	High	20MAR2023	10000	250000	1800	MODERATE RISK	20SEP2023	33	SAFE	25.000
10	Taiga	Russia	Medium	10MAY2023	8000000	4000000	600	LOW RISK	10NOV2023	31	WATCH	0.500
11	Tongass	USA	Medium	15NOV2023	68000	200000	4000	LOW RISK	15MAY2024	25	WATCH	2.941
12	Yakushima	Japan	Low	10OCT2023	505	70000	4300	LOW RISK	10APR2024	26	SAFE	138.614
13	GirForest	India	Medium	15JAN2024	1412	40000	800		.	.		.

11.PROC MEANS

proc means forest_merged;

    var Rainfall Area;

run;

/* Note: In practice above there is an Invalid in this code Find it,Correct it and Use it /*

OUTPUT:

The MEANS Procedure

Variable	N	Mean	Std Dev	Minimum	Maximum
Rainfall Area	13 13	2461.54 1386798.31	1436.16 2637717.45	600.0000000 88.0000000	5000.00 8000000.00

12.FINAL VALIDATION OUTPUT

proc print data=forest_merged;

run;

OUTPUT:

Obs	Forest_Name	Country	Fire_Risk	Survey_Date	Area	Animal_Count	Rainfall	Safety_Level	Next_Survey	Months_Since	Safety_Flag	Density
1	Amazon	Brazil	High	01JAN2023	5500000	3000000	2200	MODERATE RISK	01JUL2023	35	SAFE	0.545
2	BlackForest	Germany	Low	05APR2023	6000	150000	1200	MODERATE RISK	05OCT2023	32	SAFE	25.000
3	Borneo	Malaysia	High	18JUL2023	740000	600000	3000	LOW RISK	18JAN2024	29	SAFE	0.811
4	Congo	Congo	Medium	15FEB2023	3700000	1800000	1900	MODERATE RISK	15AUG2023	34	WATCH	0.486
5	Daintree	Australia	Medium	12JUN2023	1200	90000	3500	LOW RISK	12DEC2023	30	WATCH	75.000
6	Kinabalu	Malaysia	Medium	25AUG2023	750	120000	2800	LOW RISK	25FEB2024	28	WATCH	160.000
7	Sherwood	UK	Low	01DEC2023	423	35000	900	HIGH RISK	01JUN2024	24	SAFE	82.742
8	Sinharaja	SriLanka	Low	01SEP2023	88	65000	5000	LOW RISK	01MAR2024	27	SAFE	738.636
9	Sundarbans	India	High	20MAR2023	10000	250000	1800	MODERATE RISK	20SEP2023	33	SAFE	25.000
10	Taiga	Russia	Medium	10MAY2023	8000000	4000000	600	LOW RISK	10NOV2023	31	WATCH	0.500
11	Tongass	USA	Medium	15NOV2023	68000	200000	4000	LOW RISK	15MAY2024	25	WATCH	2.941
12	Yakushima	Japan	Low	10OCT2023	505	70000	4300	LOW RISK	10APR2024	26	SAFE	138.614
13	GirForest	India	Medium	15JAN2024	1412	40000	800		.	.		.

YESTERDAY'S QUESTION

9.PROC CORR

proc corr data=indian_states;

    var Literacy_Rate GDP Population Employment_Rate;

    table Literacy_Rate*GDP;

run;

**TABLE is not a valid statement in PROC CORR

/* Note: In practice above there is an Invalid in this code Find it,Correct it and Use it /*

OUTPUT:

The CORR Procedure

1 With Variables:	GDP
4 Variables:	Literacy_Rate GDP Population Employment_Rate

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
GDP	12	17.52500	8.57418	210.30000	7.60000	38.50000
Literacy_Rate	12	75.72500	7.59750	908.70000	66.10000	94.00000
Population	12	83.08333	55.20945	997.00000	30.00000	235.00000
Employment_Rate	12	60.85833	2.85799	730.30000	56.20000	65.30000

Pearson Correlation Coefficients, N = 12 Prob > |r| under H0: Rho=0
	Literacy_Rate	GDP	Population	Employment_Rate
GDP	0.22374 0.4845	1.00000	0.33029 0.2944	0.55222 0.0626

To Visit My Previous Electronic Data Analysis:Click Here

To Visit My Previous IPS Toppers Dataset:Click Here

To Visit My Previous Global Money Loan Trends Dataset:Click Here

To Visit My Previous Statewise Population Analysis:Click Here

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