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#### Lab7

Course: CSS 590, Fall 2008

School: Oregon State

Word Count: 815

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590 CSS Experimental Design in Agriculture Lab exercise 7th week Threeway factorial Orthogonal Contrasts Regression in the ANOVA SAS Online Documentation GLM Procedure Part I. Threeway factorial An experiment was conducted to investigate the effect of zinc and copper added to basic diets of maize or wheat fed to chicken. The treatments were arranged in a 23 factorial with four broods of chicks used as blocks....

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590 CSS Experimental Design in Agriculture Lab exercise 7th week Threeway factorial Orthogonal Contrasts Regression in the ANOVA SAS Online Documentation GLM Procedure Part I. Threeway factorial An experiment was conducted to investigate the effect of zinc and copper added to basic diets of maize or wheat fed to chicken. The treatments were arranged in a 23 factorial with four broods of chicks used as blocks. The results are expressed as average weekly gains in grams (Lab7.xls). 1) Write a SAS program to analyze the data. The model statement is an extension of the twofactor analysis we ran in part I. Remember that the vertical slash `|' is equivalent to specifying all of the possible main effects and interactions among the factors. PROC GLM; CLASS Block Zinc Copper Grain; MODEL Weight = Block Zinc|Copper|Grain; MEANS Zinc|Copper|Grain; RUN; 2) How would you interpret these results? Part II. Using contrast statements in a factorial experiment An agricultural chemist suspected that the activity of root growth hormone was dependent on temperature and concentration. He designed an experiment using two different temperature baths of a nutrient solution containing 2 ppm, 4 ppm, and 6 ppm of the root growth hormone. He had two assistants to help measure the root growth response so the experiment was run as a randomized block design with two blocks. TEMP CONC BLOCK ROOTS Cold Cold Cold Cold Cold 2 2 4 4 6 1 2 1 2 1 76 60 63 58 50 Cold Warm Warm Warm Warm Warm Warm 6 2 2 4 4 6 6 2 1 2 1 2 1 2 45 23 15 36 25 47 36 1) Copy this data into a SAS data step using the appropriate input statement and a datalines statement. 2) Develop a set of orthogonal contrasts to answer the following questions: a. b. c. d. Is there a difference in root growth due to temperature? Is there a linear response to concentration? Is there a quadratic response to concentration? Does the nature of the response to concentration (linear or quadratic) depend on the temperature? Cold, 4 ppm Cold, 6 ppm Warm, 2 ppm Warm, 4 ppm Warm, 6 ppm Cold, 2 ppm 3) Run a SAS program on this data to address these questions. PROC GLM; TITLE 'contrasts in a factorial experiment'; CLASS block temp conc; MODEL roots = block temp conc temp*conc; /*make sure that your coefficients correspond to the order of your means*/ CONTRAST CONTRAST CONTRAST CONTRAST CONTRAST 'main 'conc 'conc 'temp 'temp effect of temp' temp -1 1; lin' conc -1 0 1; quad' conc 1 -2 1; x conc lin' temp*conc 1 0 -1 -1 0 1; x conc quad' temp*conc -1 2 -1 1 -2 1; LSMEANS conc temp; LSMEANS temp*conc/out=new; PROC PLOT data=new; PLOT lsmean*conc=temp; RUN; 4) How would you interpret the output? Part Regression III: with orthogonal polynomials compared to regression on a quantitative variable (this section is optional!) An experiment was conducted to determine the effect of storage temperature on seed viability. Fifteen seed samples were obtained and three samples, selected at random from the fifteen were stored at each of five temperatures: 10, 30, 50, 70, 90. At the end of a one year storage period the samples were tested for germination percentage with the following results: Temp 10 10 10 30 30 30 50 50 50 70 70 70 90 90 90 Germination 62 55 57 26 36 31 16 15 23 10 11 18 13 11 9 1) Copy this data into a SAS data step using the appropriate input statement and a datalines statement. 2) Use Proc GLM to analyze the data as a CRD. Remember to specify that temperature is a class variable. Are there significant differences among the Temperature treatments? 3) Use Contrast statements to determine if the effect of temperature is linear, quadratic, cubic, or quartic (refer to the table of polynomial coefficients handed out in class). PROC GLM; TITLE 'Use of orthogonal polynomials'; CLASS Temp; MODEL Germination = Temp; CONTRAST 'Temp Linear' Temp -2 -1 0 1 2; CONTRAST 'Temp Quadratic' Temp 2 -1 -2 -1 2; CONTRAST 'Temp Cubic' Temp -1 2 0 -2 1; CONTRAST 'Temp Quartic' Temp 1 -4 6 -4 1; LSMEANS Temp / stderr out=new2; PROC GPLOT data=new2; PLOT lsmean*Temp; RUN; QUIT; Based on your ouput, which polynomials will you retain in your model? 4) Now try running the same regression, but consider Temperature to be a continuous rather than a class variable. PROC GLM data=yourfile; TITLE...

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