Sas code for examples from a first course in statistics
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SAS Code for Examples from a First Course in Statistics If you are running in batch mode, set options at the start of each script so that output will be formatted to fit on a letter size page. options linesize=64 pagesize=55; Do a simple probability calculation and display the result data race; pr = probnorm(-15/sqrt(325)); run; proc print data=race; var pr; run; Do a simple probability calculation and display the result with PROC IML proc iml; FF = FINV(0.05/32,2,29); print FF; quit; Compute, display and plot the ratio of confidence limits for a normal variance (Try writing a simpler version of this using PROC IML.) data chisq; input df; chirat = cinv(.995,df)/cinv(.005,df); datalines; 20 21 22 23
Sas code for examples from a first course in statistics
1. SAS Code for Examples from a First Course in Statistics If
you are running in batch mode, set options at the start of each
script so that output will be formatted to fit on a letter size
page. options linesize=64 pagesize=55; Do a simple probability
calculation and display the result data race; pr =
probnorm(-15/sqrt(325)); run; proc print data=race; var pr; run; Do
a simple probability calculation and display the result with PROC
IML proc iml; FF = FINV(0.05/32,2,29); print FF; quit; Compute,
display and plot the ratio of confidence limits for a normal
variance (Try writing a simpler version of this using PROC IML.)
data chisq; input df; chirat = cinv(.995,df)/cinv(.005,df);
datalines; 20 21 22 23 24 25 26 27 28 29 30 ; run; proc print
data=chisq; var df chirat; run; proc plot data=chisq;
2. plot chirat*df; run; Do a 2-Factor ANOVA, data entered in
the script data copper; input id warp temp pct; datalines; 1 17 50
40 2 20 50 40 3 16 50 60 4 21 50 60 5 24 50 80 6 22 50 80 9 12 75
40 10 9 75 40 11 18 75 60 12 13 75 60 13 17 75 80 14 12 75 80 25 21
125 40 26 17 125 40 27 23 125 60 28 21 125 60 29 23 125 80 30 22
125 80 ; proc anova data=copper; class temp pct; model warp= temp |
pct; run; Do a Simple Linear Regression and plot the result from
PROC REG (Plotting from PROC REG does not work in batch mode) data
crack; input id age load; datalines; 1 20 11.45 2 20 10.42 3 20
11.14 4 25 10.84 5 25 11.17 6 25 10.54 7 31 9.47 8 31 9.19 9 31
9.54 ; proc reg data=crack; model load = age; plot predicted. * age
= 'P' load * age = '*' / overlay;
3. run; Scatter plot in batch mode data crack; input id age
load; datalines; 1 20 11.45 2 20 10.42 3 20 11.14 4 25 10.84 5 25
11.17 6 25 10.54 7 31 9.47 8 31 9.19 9 31 9.54 ; proc plot
data=crack; plot load * age = "*"; run; Simple Linear Regression
and scatter plot with overlay in batch mode data crack; input id
age load; datalines; 1 20 11.45 2 20 10.42 3 20 11.14 4 25 10.84 5
25 11.17 6 25 10.54 7 31 9.47 8 31 9.19 9 31 9.54 ; proc reg
data=crack; model load = age / p; output out=crackreg p=pred
r=resid; run; proc plot data=crackreg; plot load*age="*"
pred*age="+"/ overlay; run; Simple Linear Regression ANOVA with
non-linearity test, scatter plot with overlay in batch mode data
crack; input id age load agef; datalines; 1 20 11.45 20
4. 2 20 10.42 20 3 20 11.14 20 4 25 10.84 25 5 25 11.17 25 6 25
10.54 25 7 31 9.47 31 8 31 9.19 31 9 31 9.54 31 ; proc glm
data=crack; class agef; model load = age agef / p; output
out=crackreg p=pred r=resid; run; proc plot data=crackreg; plot
load*age="*" pred*age="+"/ overlay; run; Two-Factor ANOVA, data
entered in the script data toxic; input life poison $ treatment $;
datalines; 0.31 I A 0.45 I A 0.46 I A 0.43 I A 0.36 II A 0.29 II A
0.40 II A 0.23 II A 0.22 III A 0.21 III A 0.18 III A 0.23 III A
0.82 I B 1.10 I B 0.88 I B 0.72 I B 0.92 II B 0.61 II B 0.49 II B
1.24 II B 0.30 III B 0.37 III B 0.38 III B 0.29 III B 0.43 I C 0.45
I C 0.63 I C 0.76 I C 0.44 II C 0.35 II C 0.31 II C
5. 0.40 II C 0.23 III C 0.25 III C 0.24 III C 0.22 III C 0.45 I
D 0.71 I D 0.66 I D 0.62 I D 0.56 II D 1.02 II D 0.71 II D 0.38 II
D 0.30 III D 0.36 III D 0.31 III D 0.33 III D ; run; proc anova
data=toxic; class poison treatment; model life = poison treatment
poison*treatment; run; Two-Factor ANOVA, data from a
comma-delimited text file data toxic; infile "toxic.dat" dlm=",";
input life poison $ treatment $; run; proc anova data=toxic; class
poison treatment; model life = poison treatment poison*treatment;
run; SAS Program *** EXAMPLE 1 ********************; *** Data
input, sort and print ***; **********************************;
OPTIONS PS=55 LS=77 NOCENTER NODATE NONUMBER; DATA paintdry; INFILE
CARDS MISSOVER; INPUT status $ luster hardness timeoday $; CARDS;
RUN; Fresh 7 3 Early Dried 8 9 Early Fresh 6 3 Dried 8 7 Late Fresh
5 6 Late ; PROC SORT; BY status luster hardness; RUN; PROC PRINT;
RUN;
6. *** EXAMPLE 2 ******************************; *** Data input
and means on two variables ***; *** Output statement ***;
********************************************; OPTIONS PS=51 LS=78
NOCENTER NODATE NONUMBER; data one; infile cards; input x y; cards;
run; 1 1 2 3 3 4 4 4 4 5 5 7 7 6 9 7 ; proc means MIN MAX SUM STD
USS; var x y; run; Proc print data=one; run; OPTIONS PS=31 LS=80;
Proc plot data=one; plot x*y; run; OPTIONS PS=52; *** EXAMPLE 3
********************; *** Data input, sort and print ***;
**********************************; OPTIONS PS=53 LS=79 NOCENTER
NODATE NONUMBER; DATA NEW3; INFILE CARDS MISSOVER; INPUT day number
type $ model $; CARDS; RUN; 17 9 TRUCKS SEMI 18 8 TRUCKS SEMI 19 2
TRUCKS PICKUP 22 4 TRUCKS SEMI 16 3 CARS COUPE 17 2 CARS COUPE 18 3
CARS SEDAN 19 1 CARS SEDAN 22 5 CARS SEDAN 17 1 VANS 5DOOR 17 4
VANS 4DOOR 19 2 VANS 5DOOR ; PROC SORT DATA=NEW3; BY type model day
number; RUN; TITLE1 'My raw data is listed below'; PROC PRINT
DATA=NEW3 double; VAR type model day number; RUN; PROC SORT
DATA=NEW3; BY TYPE; RUN; TITLE1 'Selected means are provided
below'; PROC MEANS DATA=NEW3; BY type; VAR number day; RUN; PROC
SORT DATA=NEW3; BY type; RUN; PROC MEANS DATA=NEW3 NOPRINT; BY
type; VAR number day; OUTPUT OUT=THREE N=NNo DNo MEAN=NMEAN DMEAN
VAR=NVAR DVAR; RUN; TITLE1 'Outputted means are listed below';
7. PROC PRINT DATA=THREE; VAR TYPE NNo DMEAN NVAR DNo NMEAN
DVAR; RUN; *** EXAMPLE 4
********************************************; *** Reading a file
and saving a permanent SAS data set ***;
**********************************************************; OPTIONS
PS=55 LS=77 NOCENTER NODATE NONUMBER; libname mylib 'A:'; DATA
mylib.OLD_DATA; INFILE CARDS MISSOVER; INPUT MONTH DAY YEAR STATION
$ SPECIES $ NUMBER; LABEL STATION = 'Sample stations'; LABEL
SPECIES = 'Species common name'; LABEL STATION = 'Number caught';
CARDS; RUN; 01 8 97 North Spot 8 01 8 97 North Croaker 3 01 8 97
South Spot 11 03 23 97 North Spot 2 03 23 97 South Spot 5 05 15 97
North Spot 1 05 15 97 North Croaker 3 05 15 97 South Spot 17 05 15
97 South Croaker 2 08 12 97 North Spot 8 08 12 97 North Croaker 3
08 12 97 North RedDrum 1 08 12 97 North Spot 8 08 12 97 North
Croaker 9 ; *** EXAMPLE 5 **************************; *** Reading a
permanent SAS data set ***; *** Concatenating SAS data sets ***;
****************************************; OPTIONS PS=55 LS=77
NOCENTER NODATE NUMBER PAGENO=1; libname mylib 'A:'; TITLE1
'Example program #5'; DATA NEW_DATA; INFILE CARDS MISSOVER; INPUT
MONTH DAY YEAR STATION $ SPECIES $ NUMBER; CARDS; RUN; 01 14 98
North Spot 12 01 14 98 North Croaker 1 01 14 98 North RedDrum 4 01
14 98 South Spot 5 03 6 98 South Spot 3 03 6 98 South Croaker 9 05
26 98 North Spot 11 05 26 98 North Croaker 12 05 26 98 South Spot 4
07 29 98 North Spot 24 07 29 98 North Croaker 16 07 29 98 North
Spot 12 07 29 98 North Croaker 7 ; DATA MYLIB.ALL_DATA; SET
mylib.old_data NEW_DATA; sasdate = mdy(month, day, year); format
sasdate date7.;
8. RUN; PROC SORT DATA=MYLIB.ALL_DATA; BY SPECIES YEAR MONTH
DAY; RUN; PROC PRINT DATA=MYLIB.ALL_DATA; TITLE2 'Raw data listing
sorted by species y m d'; VAR SPECIES sasdate STATION NUMBER; RUN;
PROC FREQ DATA=MYLIB.ALL_DATA; BY SPECIES; WEIGHT NUMBER; TITLE2
'Species frequency (weighted by number)'; TABLE MONTH*STATION; RUN;
PROC FREQ DATA=MYLIB.ALL_DATA; WEIGHT NUMBER; TITLE2 'Species
frequency - chi square test'; TABLE MONTH*STATION / chisq cellchi2
norow nocol nopercent; RUN; proc plot data=mylib.all_data; TITLE2
'Scatter plot of number by date'; plot number*sasdate=species; run;
proc chart data=mylib.all_data; by species; TITLE2 'Horizontal bar
chart'; hbar species / sumvar=number group=station type=sum; run;
OPTIONS PS=30 LS=88; proc chart data=mylib.all_data; TITLE2
'Histogram'; vbar species / sumvar=number type=mean; run; SAS Log 1
*** EXAMPLE 1 ********************; 2 *** Data input, sort and
print ***; 3 **********************************; 4 OPTIONS PS=55
LS=77 NOCENTER NODATE NONUMBER; 5 DATA paintdry; INFILE CARDS
MISSOVER; 6 INPUT status $ luster hardness timeoday $; 7 CARDS;
NOTE: The data set WORK.PAINTDRY has 5 observations and 4
variables. NOTE: The DATA statement used 0.05 seconds. 7 RUN; 13 ;
14 PROC SORT; BY status luster hardness; RUN; NOTE: The data set
WORK.PAINTDRY has 5 observations and 4 variables. NOTE: The
PROCEDURE SORT used 0.05 seconds. 15 PROC PRINT; RUN; NOTE: The
PROCEDURE PRINT printed page 1. NOTE: The PROCEDURE PRINT used 0.05
seconds. 16 17 18 *** EXAMPLE 2
******************************;
9. 19 *** Data input and means on two variables ***; 20 ***
Output statement ***; 21
********************************************; 22 OPTIONS PS=51
LS=78 NOCENTER NODATE NONUMBER; 23 data one; infile cards; 24 input
x y; 25 cards; NOTE: The data set WORK.ONE has 8 observations and 2
variables. NOTE: The DATA statement used 0.05 seconds. 25 run; 34 ;
35 proc means MIN MAX SUM STD USS; var x y; run; NOTE: The
PROCEDURE MEANS printed page 2. NOTE: The PROCEDURE MEANS used 0.0
seconds. 36 Proc print data=one; run; NOTE: The PROCEDURE PRINT
printed page 3. NOTE: The PROCEDURE PRINT used 0.0 seconds. 37
OPTIONS PS=31 LS=80; 38 Proc plot data=one; plot x*y; run; 39
OPTIONS PS=52; 40 41 *** EXAMPLE 3 ********************; 42 ***
Data input, sort and print ***; 43
**********************************; 44 OPTIONS PS=53 LS=79 NOCENTER
NODATE NONUMBER; NOTE: The PROCEDURE PLOT printed page 4. NOTE: The
PROCEDURE PLOT used 0.0 seconds. 45 DATA NEW3; INFILE CARDS
MISSOVER; 46 INPUT day number type $ model $; 47 CARDS; NOTE: The
data set WORK.NEW3 has 12 observations and 4 variables. NOTE: The
DATA statement used 0.05 seconds. 47 RUN; 60 ; 61 PROC SORT
DATA=NEW3; BY type model day number; RUN; NOTE: The data set
WORK.NEW3 has 12 observations and 4 variables. NOTE: The PROCEDURE
SORT used 0.05 seconds. 62 TITLE1 'My raw data is listed below'; 63
PROC PRINT DATA=NEW3 double; VAR type model day number; RUN; NOTE:
The PROCEDURE PRINT printed page 5. NOTE: The PROCEDURE PRINT used
0.0 seconds. 64 65 PROC SORT DATA=NEW3; BY TYPE; RUN; NOTE: Input
data set is already sorted, no sorting done. NOTE: The PROCEDURE
SORT used 0.0 seconds. 66 TITLE1 'Selected means are provided
below'; 67 PROC MEANS DATA=NEW3; BY type; VAR number day; RUN;
NOTE: The PROCEDURE MEANS printed page 6. NOTE: The PROCEDURE MEANS
used 0.0 seconds. 68 69 PROC SORT DATA=NEW3; BY type; RUN; NOTE:
Input data set is already sorted, no sorting done. NOTE: The
PROCEDURE SORT used 0.0 seconds. 70 PROC MEANS DATA=NEW3 NOPRINT;
BY type; VAR number day; 71 OUTPUT OUT=THREE N=NNo DNo MEAN=NMEAN
DMEAN VAR=NVAR DVAR; RUN; NOTE: The data set WORK.THREE has 3
observations and 9 variables. NOTE: The PROCEDURE MEANS used 0.0
seconds.
10. 72 TITLE1 'Outputted means are listed below'; 73 PROC PRINT
DATA=THREE; VAR TYPE NNo DMEAN NVAR DNo NMEAN DVAR; RUN; NOTE: The
PROCEDURE PRINT printed page 7. NOTE: The PROCEDURE PRINT used 0.0
seconds. 74 75 76 *** EXAMPLE 4
********************************************; 77 *** Reading a file
and saving a permanent SAS data set ***; 78
**********************************************************; 79
OPTIONS PS=55 LS=77 NOCENTER NODATE NONUMBER; 80 libname mylib
'A:'; NOTE: Libref MYLIB was successfully assigned as follows:
Engine: V612 Physical Name: A: 81 DATA mylib.OLD_DATA; INFILE CARDS
MISSOVER; 82 INPUT MONTH DAY YEAR STATION $ SPECIES $ NUMBER; 83
LABEL STATION = 'Sample stations'; 84 LABEL SPECIES = 'Species
common name'; 85 LABEL STATION = 'Number caught'; 86 CARDS; NOTE:
The data set MYLIB.OLD_DATA has 14 observations and 6 variables.
NOTE: The DATA statement used 5.21 seconds. 86 RUN; 101 ; 102 103
*** EXAMPLE 5 **************************; 104 *** Reading a
permanent SAS data set ***; 105 *** Concatenating SAS data sets
***; 106 ****************************************; 107 OPTIONS
PS=55 LS=77 NOCENTER NODATE NUMBER PAGENO=1; 108 libname mylib
'A:'; NOTE: Libref MYLIB was successfully assigned as follows:
Engine: V612 Physical Name: A: 109 110 TITLE1 'Example program #5';
111 DATA NEW_DATA; INFILE CARDS MISSOVER; 112 INPUT MONTH DAY YEAR
STATION $ SPECIES $ NUMBER; 113 CARDS; NOTE: The data set
WORK.NEW_DATA has 13 observations and 6 variables. NOTE: The DATA
statement used 0.05 seconds. 113 RUN; 127 ; 128 DATA
MYLIB.ALL_DATA; SET mylib.old_data NEW_DATA; 129 sasdate =
mdy(month, day, year); format sasdate date7.; 130 RUN; NOTE: The
data set MYLIB.ALL_DATA has 27 observations and 7 variables. NOTE:
The DATA statement used 4.55 seconds. 131 PROC SORT
DATA=MYLIB.ALL_DATA; BY SPECIES YEAR MONTH DAY; RUN; NOTE: The data
set MYLIB.ALL_DATA has 27 observations and 7 variables. NOTE: The
PROCEDURE SORT used 4.16 seconds. 132 PROC PRINT
DATA=MYLIB.ALL_DATA; 133 TITLE2 'Raw data listing sorted by species
y m d'; 134 VAR SPECIES sasdate STATION NUMBER; 135 RUN; NOTE: The
PROCEDURE PRINT printed page 1.
11. NOTE: The PROCEDURE PRINT used 0.0 seconds. 136 PROC FREQ
DATA=MYLIB.ALL_DATA; BY SPECIES; WEIGHT NUMBER; 137 TITLE2 'Species
frequency (weighted by number)'; 138 TABLE MONTH*STATION; 139 RUN;
NOTE: The PROCEDURE FREQ printed pages 2-4. NOTE: The PROCEDURE
FREQ used 0.05 seconds. 140 PROC FREQ DATA=MYLIB.ALL_DATA; WEIGHT
NUMBER; 141 TITLE2 'Species frequency - chi square test'; 142 TABLE
MONTH*STATION / chisq cellchi2 norow nocol nopercent; 143 RUN;
NOTE: The PROCEDURE FREQ printed page 5. NOTE: The PROCEDURE FREQ
used 0.05 seconds. 144 145 proc plot data=mylib.all_data; 146
TITLE2 'Scatter plot of number by date'; 147 plot
number*sasdate=species; 148 run; NOTE: The PROCEDURE PLOT printed
page 6. NOTE: The PROCEDURE PLOT used 0.0 seconds. 149 proc chart
data=mylib.all_data; by species; 150 TITLE2 'Horizontal bar chart';
151 hbar species / sumvar=number group=station type=sum; 152 run;
NOTE: The PROCEDURE CHART printed pages 7-9. NOTE: The PROCEDURE
CHART used 0.05 seconds. 153 OPTIONS PS=30 LS=88; 154 proc chart
data=mylib.all_data; 155 TITLE2 'Histogram'; 156 vbar species /
sumvar=number type=mean; 157 run; NOTE: The PROCEDURE CHART printed
page 10. NOTE: The PROCEDURE CHART used 0.0 seconds. NOTE: SAS
Institute Inc., SAS Campus Drive, Cary, NC USA 27513-2414 SAS
Output The SAS System OBS STATUS LUSTER HARDNESS TIMEODAY 1 Dried 8
7 Late 2 Dried 8 9 Early 3 Fresh 5 6 Late 4 Fresh 6 3 5 Fresh 7 3
Early The SAS System Variable Minimum Maximum Sum Std Dev USS
------------------------------------------------------------------------------
X 1.0000000 9.0000000 35.0000000 2.6152028 201.0000000 Y 1.0000000
7.0000000 37.0000000 2.0658793 201.0000000
------------------------------------------------------------------------------
12. The SAS System OBS X Y 1 1 1 2 2 3 3 3 4 4 4 4 5 4 5 6 5 7
7 7 6 8 9 7 The SAS System Plot of X*Y. Legend: A = 1 obs, B = 2
obs, etc. X 9 A 8 7 A 6 5 A 4 A A 3 A 2 A 1 A 1 2 3 4 5 6 7 Y My
raw data is listed below OBS TYPE MODEL DAY NUMBER 1 CARS COUPE 16
3 2 CARS COUPE 17 2 3 CARS SEDAN 18 3 4 CARS SEDAN 19 1 5 CARS
SEDAN 22 5
13. 6 TRUCKS PICKUP 19 2 7 TRUCKS SEMI 17 9 8 TRUCKS SEMI 18 8
9 TRUCKS SEMI 22 4 10 VANS 4DOOR 17 4 11 VANS 5DOOR 17 1 12 VANS
5DOOR 19 2 Selected means are provided below TYPE=CARS Variable N
Mean Std Dev Minimum Maximum
--------------------------------------------------------------------
NUMBER 5 2.8000000 1.4832397 1.0000000 5.0000000 DAY 5 18.4000000
2.3021729 16.0000000 22.0000000
--------------------------------------------------------------------
TYPE=TRUCKS Variable N Mean Std Dev Minimum Maximum
--------------------------------------------------------------------
NUMBER 4 5.7500000 3.3040379 2.0000000 9.0000000 DAY 4 19.0000000
2.1602469 17.0000000 22.0000000
--------------------------------------------------------------------
TYPE=VANS Variable N Mean Std Dev Minimum Maximum
--------------------------------------------------------------------
NUMBER 3 2.3333333 1.5275252 1.0000000 4.0000000 DAY 3 17.6666667
1.1547005 17.0000000 19.0000000
--------------------------------------------------------------------
Outputted means are listed below OBS TYPE NNO DMEAN NVAR DNO NMEAN
DVAR 1 CARS 5 18.4000 2.2000 5 2.80000 5.30000 2 TRUCKS 4 19.0000
10.9167 4 5.75000 4.66667 3 VANS 3 17.6667 2.3333 3 2.33333 1.33333
Example program #5 1 Raw data listing sorted by species y m d OBS
SPECIES SASDATE STATION NUMBER 1 Croaker 08JAN97 North 3 2 Croaker
15MAY97 North 3 3 Croaker 15MAY97 South 2
14. 4 Croaker 12AUG97 North 3 5 Croaker 12AUG97 North 9 6
Croaker 14JAN98 North 1 7 Croaker 06MAR98 South 9 8 Croaker 26MAY98
North 12 9 Croaker 29JUL98 North 16 10 Croaker 29JUL98 North 7 11
RedDrum 12AUG97 North 1 12 RedDrum 14JAN98 North 4 13 Spot 08JAN97
North 8 14 Spot 08JAN97 South 11 15 Spot 23MAR97 North 2 16 Spot
23MAR97 South 5 17 Spot 15MAY97 North 1 18 Spot 15MAY97 South 17 19
Spot 12AUG97 North 8 20 Spot 12AUG97 North 8 21 Spot 14JAN98 North
12 22 Spot 14JAN98 South 5 23 Spot 06MAR98 South 3 24 Spot 26MAY98
North 11 25 Spot 26MAY98 South 4 26 Spot 29JUL98 North 24 27 Spot
29JUL98 North 12 Example program #5 2 Species frequency (weighted
by number) Species common name=Croaker TABLE OF MONTH BY STATION
MONTH STATION(Number caught) Frequency Percent Row Pct Col Pct
North South Total 1 4 0 4 6.15 0.00 6.15 100.00 0.00 7.41 0.00 3 0
9 9 0.00 13.85 13.85 0.00 100.00 0.00 81.82 5 15 2 17 23.08 3.08
26.15 88.24 11.76 27.78 18.18
15. 7 23 0 23 35.38 0.00 35.38 100.00 0.00 42.59 0.00 8 12 0 12
18.46 0.00 18.46 100.00 0.00 22.22 0.00 Total 54 11 65 83.08 16.92
100.00 Example program #5 3 Species frequency (weighted by number)
Species common name=RedDrum TABLE OF MONTH BY STATION MONTH
STATION(Number caught) Frequency Percent Row Pct Col Pct North
Total 1 4 4 80.00 80.00 100.00 80.00 8 1 1 20.00 20.00 100.00 20.00
Total 5 5 100.00 100.00 Example program #5 4 Species frequency
(weighted by number) Species common name=Spot TABLE OF MONTH BY
STATION MONTH STATION(Number caught) Frequency Percent Row Pct Col
Pct North South Total
16. 1 20 16 36 15.27 12.21 27.48 55.56 44.44 23.26 35.56 3 2 8
10 1.53 6.11 7.63 20.00 80.00 2.33 17.78 5 12 21 33 9.16 16.03
25.19 36.36 63.64 13.95 46.67 7 36 0 36 27.48 0.00 27.48 100.00
0.00 41.86 0.00 8 16 0 16 12.21 0.00 12.21 100.00 0.00 18.60 0.00
Total 86 45 131 65.65 34.35 100.00 Example program #5 5 Species
frequency - chi square test TABLE OF MONTH BY STATION MONTH
STATION(Number caught) Frequency Cell Chi-SquareNorth South Total 1
28 16 44 0.441 1.1418 3 2 17 19 9.9983 25.888 5 27 23 50 2.2805
5.905 7 59 0 59 6.3484 16.438 8 29 0 29 3.1204 8.0796 Total 145 56
201
17. STATISTICS FOR TABLE OF MONTH BY STATION Statistic DF Value
Prob Chi-Square 4 79.641 0.001 Likelihood Ratio Chi-Square 4 98.373
0.001 Mantel-Haenszel Chi-Square 1 35.363 0.001 Phi Coefficient
0.629 Contingency Coefficient 0.533 Cramer's V 0.629 Sample Size =
201 Example program #5 6 Scatter plot of number by date Plot of
NUMBER*SASDATE. Symbol is value of SPECIES. NUMBER 24 S 23 22 21 20
19 18 17 S 16 C 15 14 13 12 S C S 11 S S 10 9 C C 8 S S 7 C 6 5 S S
4 R S 3 C C C S 2 S C 1 S R C 17DEC96 27MAR97 05JUL97 13OCT97
21JAN98 01MAY98 09AUG98 SASDATE NOTE: 1 obs hidden. Example program
#5 7 Horizontal bar chart
18. Species common name=Croaker STATION Species common name
NUMBER Freq Sum North Croaker *************************** 8
54.00000 South Croaker ****** 2 11.00000 10 20 30 40 50 NUMBER Sum
Example program #5 8 Horizontal bar chart Species common
name=RedDrum STATION Species common name NUMBER Freq Sum North
RedDrum ************************* 2 5.000000 1 2 3 4 5 NUMBER Sum
Example program #5 9 Horizontal bar chart Species common name=Spot
STATION Species common name NUMBER Freq Sum North Spot
********************************** 9 86.00000 South Spot
****************** 6 45.00000 10 20 30 40 50 60 70 80 NUMBER Sum
Example program #5 10 Histogram NUMBER Mean ***** 8 ***** *****
***** ***** *****
