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Homework 5 Solutions
Assigned: 29 September 2004
Due: 06 October 2004
C:\Documents and Settings\John Bailer\My Documents\baileraj\Classes\Fall 2004\sta402\hw\
Homework-5.doc
1. Refer to the 4 group (packaging method)- log-bacterial growth study. In this exercise, you
will use SAS/GRAPH to construct figures displaying the means and standard deviations.
a. Construct a figure containing side-by-side boxplots using the INTERPOL=BOX option
associated with a SYMBOL statement used with PROC GPLOT.
ODS RTF file='D:\baileraj\Classes\Fall 2003\sta402\hw\hw5-prob1a.rtf’; proc gplot data=class.meat; title h= 1.5 ‘Plot of log(Bacterial count) vs. packaging condition'; title2 h= 1 '[boxplot is plotted for each condition]'; symbol1 interpol=box /* plots +/- 2 SD from the mean at each conc / value=dot; plot logcountcondition; run ; ODS RTF CLOSE;
b. Construct a figure with the data points plotted. Superimpose the mean plus/minus 2 std.
deviation segments on this plot.
ODS RTF file='D:\baileraj\Classes\Fall 2003\sta402\hw\hw5-prob1b.rtf’; proc gplot data=class.meat; title h= 1.5 ‘Plot of log(Bacterial count) vs. packaging condition'; title2 h= 1 '[mean +/- 2SD is plotted for each condition]'; symbol1 interpol=STD2T /* plots +/- 2 SD from the mean at each conc / / T= add top and bottom to each 2 SD diff / value=dot; plot logcountcondition;
If you want the error bars to represent a given number C of standard errors instead of a
confidence interval, and if the number of observations assigned to each midpoint is the
same, then you can find the appropriate value for the CLM= option by running a DATA
step. For example, if you want error bars that represent one standard error (C=1) with a
sample size of N , you can run the following DATA step to compute the appropriate
value for the CLM= option and assign that value to a macro variable &LEVEL:
data null; c = 1; n = 10; level = 100 * (1 - 2 * (1 - probt( c, n-1))); put all; call symput('level',put(level,best12.)); run;
data null; c = 1; n = 3; level = 100 * (1 - 2 * (1 - probt( c, n-1))); put all; call symput('level',put(level,best12.)); run; c=1 n=3 level=57.735026919 ERROR=0 N= ODS RTF file='D:\baileraj\Classes\Fall 2003\sta402\hw\hw5-fig1c.rtf'; goptions reset=global gunit=pct border cback=white colors=(black blue green red) ftext=swiss ftitle=swissb htitle= 5 htext= 3.5 ; title1 'Average log(Bacteria count) by Packaging Condition'; axis1 label=('log(count)' minor=(number= 1 ); axis2 label=('Packaging' j=r 'Condition'); pattern1 color=cyan; proc gchart data=class.meat; hbar condition / type=mean sumvar=logcount /* freqlabel='Number in Group' / / meanlabel='Mean Number Young' */ errorbar=bars clm= &level raxis=axis maxis=axis noframe coutline=black; run ; ODS RTF CLOSE;
ODS RTF file='D:\baileraj\Classes\Fall 2003\sta402\hw\hw5-fig1c-2.rtf'; goptions reset=global gunit=pct border cback=white colors=(black blue green red) ftext=swiss ftitle=swissb htitle= 5 htext= 3.5 ; title1 'Average log(Bacteria count) by Packaging Condition'; axis1 label=('log(count)' minor=(number= 1 ); axis2 label=('Packaging' j=r 'Condition'); pattern1 color=cyan;
2. Refer to the nitrofen data. Generate a plot of the nitrofen data (sqrt total as response) vs.
concentration. Superimpose the fitted quadratic model. Only use GPLOT to generate this figure
- i.e. don’t use PROC REG to generate fits first, I want you to use the SYMBOL INTERPOL reg
options.
libname class 'D:\baileraj\Classes\Fall 2003\sta402\data’;
options ls=75;
Data new_nitro; set class.nitrofen; Sqrt_total = sqrt(total); run; ODS RTF file='D:\baileraj\Classes\Fall 2003\sta402\hw\hw5-prob2.rtf’; proc gplot data=new_nitro ; title h= 1.4 'Plot of the number of C. dubia young versus Nitrofen Concentration'; title2 h= 1 '[Regression line for quadratic model displayed]'; symbol1 interpol=rq /* r=regression, l=linear (q,c also possible), clm=conf. int. mean (cli option), 95= conf. level / value=diamond height= 3 cv=red ci=blue co=green width= 2 ; plot sqrt_totalconc / hminor= 1 overlay regeqn; /* adds regression eqn to bottom left of plot */ run ; ODS RTF CLOSE;
stotal = sqrt(total); c_conc = conc – 157; * center the concentration; c_conc2 = c_conc*2; / proc means; var conc; run; */ ods html; ods graphics on; proc reg; model stotal = c_conc c_conc2; run; ods graphics off; ods html close;
The SAS System
The REG Procedure
Model: MODEL
Dependent Variable: stotal
Number of Observations Read 50
Number of Observations Used 50
Analysis of Variance
Source DF Sum of
Squares
Mean
Square
F Value Pr>F
Model 2 81.29572 40.64786 122.57 <.
Error 47 15.58676 0.
Corrected Total 49 96.
Root MSE 0.57588 R-Square 0.
Dependent Mean 4.57628 Adj R-Sq 0.
Coeff Var 12.
Parameter Estimates
Variable DF Parameter
Estimate
Standard
Error
tValue Pr>|t|
Intercept 1 5.25168 0.12697 41.36 <.
c_conc 1 -0.01068 0.00074385 -14.36 <.
c_conc2 1 -0.00005621 0.00000811 -6.93 <.
4. Find a data set that interests you and generate a display that best represents it using
SAS/GRAPH.
