Presentation is loading. Please wait.

Presentation is loading. Please wait.

Hormone Example: nknw892.sas Y = change in growth rate after treatment Factor A = gender (male, female) Factor B = bone development level (severely depressed,

Published byPaula Harvey Modified over 8 years ago

Similar presentations

Presentation on theme: "Hormone Example: nknw892.sas Y = change in growth rate after treatment Factor A = gender (male, female) Factor B = bone development level (severely depressed,"— Presentation transcript:

1 Hormone Example: nknw892.sas Y = change in growth rate after treatment Factor A = gender (male, female) Factor B = bone development level (severely depressed, moderately depressed, mildly depressed) n ij j 1:severely2:moderately3:mildly i 1: male322 2: female133

2 Hormone Example: Input data hormone; infile ‘H:\My Documents\Stat 512\CH23TA01.DAT'; input growth gender bone; proc print data=hormone; run; Obsgrowthgenderbone 11.411 22.411 32.211 42.112 51.712 60.713 71.113 82.421 92.522 101.822 112.022 120.523 130.923 141.323

3 Hormone Example: Scatterplot data hormone; set hormone; if (gender eq 1)*(bone eq 1) then gb='1_Msev '; if (gender eq 1)*(bone eq 2) then gb='2_Mmod '; if (gender eq 1)*(bone eq 3) then gb='3_Mmild'; if (gender eq 2)*(bone eq 1) then gb='4_Fsev '; if (gender eq 2)*(bone eq 2) then gb='5_Fmod '; if (gender eq 2)*(bone eq 3) then gb='6_Fmild'; run; title1 h=3 'Scatterplot Hormone Example'; axis1 label=(h=2); axis2 label=(h=2 angle=90); symbol1 v=circle i=none c=blue; proc gplot data=hormone; plot growth*gb/haxis=axis1 vaxis=axis2; run;

4 Hormone Example: Scatterplot (cont)

5 Hormone Example: Means/Interaction proc means data=hormone; output out=means mean=avgrowth; by gender bone; title1 h=3 'Plot of the means'; symbol1 v='M' i=join c=black h=1.5; symbol2 v='F' i=join c=purple h=1.5; proc gplot data=means; plot avgrowth*bone=gender/haxis=axis1 vaxis=axis2; run; symbol1 v='S' i=join c=black h=1.5; symbol2 v='M' i=join c=red h=1.5; symbol3 v='L' i=join c=blue h=1.5; proc gplot data=means; plot avgrowth*gender=bone/haxis=axis1 vaxis=axis2; run;

6 Hormone Example: Means (cont) gender=1 bone=2 gender=1 bone=3 gender=2 bone=1 gender=2 bone=2 gender=2 bone=3 Analysis Variable : growth NMeanStd DevMinimumMaximum gender=1 bone=1 32.00000000.52915031.40000002.4000000 21.90000000.28284271.70000002.1000000 20.90000000.28284270.70000001.1000000 12.4000000. 32.10000000.36055511.80000002.5000000 30.90000000.40000000.50000001.3000000

7 Hormone Example: Interaction (cont)

8

9 Hormone Example: Residual Plots

10 Hormone Example: Normality plots

11 Hormone Example: ANOVA proc glm data=hormone; class gender bone; model growth=gender|bone/solution; means gender*bone; SourceDFSum of SquaresMean SquareF ValuePr > F Model54.474285710.894857145.510.0172 Error81.300000000.16250000 Corrected Total135.77428571 R-SquareCoeff VarRoot MSEgrowth Mean 0.77486424.537310.4031131.642857

12 Hormone Example: cell means Level of gender Level of bone N growth MeanStd Dev 1132.000000000.52915026 1221.900000000.28284271 1320.900000000.28284271 2112.40000000. 2232.100000000.36055513 2330.900000000.40000000

13 Hormone Example: Factor Effects ParameterEstimateStandard Errort ValuePr > |t| Intercept0.900000000B0.232737333.870.0048 gender 1-0.000000000B0.36799004-0.001.0000 gender 20.000000000B... bone 11.500000000B0.465474673.220.0122 bone 21.200000000B0.329140293.650.0065 bone 30.000000000B... gender*bone 1 1-0.400000000B0.59336610-0.670.5192 gender*bone 1 2-0.200000000B0.52041650-0.380.7108 gender*bone 1 30.000000000B... gender*bone 2 10.000000000B... gender*bone 2 20.000000000B... gender*bone 2 30.000000000B...

14 Hormone Example: SS SourceDFType I SSMean SquareF ValuePr > F gender10.00285714 0.020.8978 bone24.396000002.1980000013.530.0027 gender*bone20.075428570.037714290.230.7980 SourceDFType III SSMean SquareF ValuePr > F gender10.12000000 0.740.4152 bone24.189714292.0948571412.890.0031 gender*bone20.075428570.037714290.230.7980

15 Hormone Example: Contrast gender*bone contrast 'gender*bone Type I and III' gender*bone 1 -1 0 -1 1 0, gender*bone 0 1 -1 0 -1 1; SourceDFType I SSMean SquareF ValuePr > F gender*bone20.075428570.037714290.230.7980 SourceDFType III SSMean SquareF ValuePr > F gender*bone20.075428570.037714290.230.7980 ContrastDFContrast SSMean SquareF ValuePr > F gender*bone Type I and III20.075428570.037714290.230.7980

16 Hormone Example: Contrast gender Type III contrast 'gender Type III' gender 3 -3 gender*bone 1 1 1 -1 -1 -1; estimate 'gender Type III' gender 3 -3 gender*bone 1 1 1 -1 -1 -1; SourceDFType III SSMean SquareF ValuePr > F gender10.12000000 0.740.4152 ContrastDFContrast SSMean SquareF ValuePr > F gender Type III10.12000000 0.740.4152 ParameterEstimateStandard Errort ValuePr > |t| gender Type III-0.600000000.69821200-0.860.4152

17 Hormone Example: Contrast gender Type I contrast 'gender Type I' gender 7 -7 bone 2 -1 -1 gender*bone 3 2 2 -1 -3 -3; estimate 'gender Type I' gender 7 -7 bone 2 -1 -1 gender*bone 3 2 2 -1 -3 -3; ContrastDFContrast SSMean SquareF ValuePr > F gender Type I10.00285714 0.020.8978 ParameterEstimateStandard Errort ValuePr > |t| gender Type I0.200000001.508310310.130.8978 SourceDFType I SSMean SquareF ValuePr > F gender10.00285714 0.020.8978

18 Hormone Example: Contrast Bone III contrast 'bone Type III' bone 2 -2 0 gender*bone 1 -1 0 1 -1 0, bone 0 2 -2 gender*bone 0 1 -1 0 1 -1; ContrastDFContrast SSMean SquareF ValuePr > F bone Type III24.189714292.0948571412.890.0031 SourceDFType III SSMean SquareF ValuePr > F bone24.189714292.0948571412.890.0031

19 Hormone Example: Contrast Bone I contrast 'bone Type I' gender 7 -7 bone 20 -20 0 gender*bone 15 -8 0 5 -12 0, bone 0 5 -5 gender*bone 0 2 -2 0 3 -3; ContrastDFContrast SSMean SquareF ValuePr > F bone Type I24.306285712.1531428613.250.0029 SourceDFType I SSMean SquareF ValuePr > F bone24.396000002.1980000013.530.0027 bone first24.306285712.1531428613.250.0029

20 Hormone Example: SS SourceDFType I SSMean SquareF ValuePr > F gender10.00285714 0.020.8978 bone24.396000002.1980000013.530.0027 gender*bone20.075428570.037714290.230.7980 SourceDFType III SSMean SquareF ValuePr > F gender10.12000000 0.740.4152 bone24.189714292.0948571412.890.0031 gender*bone20.075428570.037714290.230.7980

21 Hormone Example: Interaction (cont)

22 Hormone Example: with pooling proc glm data=hormone; class gender bone; model growth=gender bone/solution; means gender bone/ tukey lines; run;

23 Hormone Example: with pooling (cont) SourceDFSum of SquaresMean SquareF ValuePr > F Model34.398857141.4662857110.660.0019 Error101.375428570.13754286 Corrected Total135.77428571 R-SquareCoeff VarRoot MSEgrowth Mean 0.76180122.574560.3708681.642857 SourceDFType I SSMean SquareF ValuePr > F gender10.00285714 0.020.8883 bone24.396000002.1980000015.980.0008 SourceDFType III SSMean SquareF ValuePr > F gender10.09257143 0.670.4311 bone24.396000002.1980000015.980.0008

24 Hormone Example: with pooling (cont) ParameterEstimateStandard Errort ValuePr > |t| Intercept0.968571429B0.185727965.220.0004 gender 1-0.171428571B0.20896028-0.820.4311 gender 20.000000000B... bone 11.260000000B0.259312894.860.0007 bone 21.120000000B0.234557334.770.0008 bone 30.000000000B...

25 Hormone Example: multiple comparisons Note:Cell sizes are not equal. Means with the same letter are not significantly different. Tukey Grouping MeanNbone A2.100041 A A2.020052 B0.900053

26 Interaction plot

27 3-way ANOVA Table

28 Test Statistics for 3-way ANOVA

29 Exercise Example: nknw943.sas Y = exercise tolerance Factor A = gender (male, female) Factor B = percent body fat (low, high) Factor C = smoking history (light, heavy) n = 3

30 Exercise Example: input goptions htext=2; data exercise; infile H:\My Documents\Stat 512\CH24TA04.DAT'; input extol gender fat smoke; data exercise; set exercise; gfs = 100*gender + 10*fat + smoke; proc print data=exercise; run;

31 Exercise Example: input (cont) Obsextolgenderfatsmokegfs 124.1111111 229.2111111 324.6111111 420.0211211 521.9211211 617.6211211 714.6121121 815.3121121 912.3121121 1016.1221221 119.3221221 1210.8221221 1317.6112112 1418.8112112 1523.2112112 1614.8212212 1710.3212212 1811.3212212 1914.9122122 2020.4122122 2112.8122122 2210.1222222 2314.4222222 246.1222222

32 Exercise Example: Scatterplot proc sort data=exercise; by gfs; run; title1 h=3 'Scatterplot'; axis2 label=(h=2 angle=90); symbol1 v=circle i=none c=blue; proc gplot data=exercise; plot extol*gfs/ haxis = 111 112 121 122 211 212 221 222 vaxis=axis2; run;

33 Exercise Example: Scatterplot (cont)

34 Exercise Example: Interaction Plot proc sort data=exercise; by gender fat smoke; proc means data=exercise; output out=exer2 mean=avextol; by gender fat smoke; data exer2; set exer2; fs = fat*10 + smoke; proc print data=exer2; run; Obsgenderfatsmoke_TYPE__FREQ_avextolfs 11110325.966711 21120319.866712 31210314.066721 41220316.033322 52110319.833311 62120312.133312 72210312.066721 82220310.200022

35 Exercise Example: Interaction Plot (cont) title1 h=3 'Interaction Plot'; proc sort data=exer2; by fs; symbol1 v='M' i=join c=blue height=1.5; symbol2 v='F' i=join c=purple height=1.5; proc gplot data=exer2; plot avextol*fs=gender / haxis = 11 12 21 22 vaxis=axis2; run;

36 Exercise Example: Interaction Plot (cont)

37 Exercise Example: ANOVA – full model proc glm data=exercise; class gender fat smoke; model extol=gender|fat|smoke / solution; means gender*fat*smoke; output out=diag r = resid p = pred; run;

38 Exercise Example: Residual Plots

39 Exercise Example: Normality Plots

40 Exercise Example: ANOVA table SourceDFSum of SquaresMean SquareF ValuePr > F Model7588.582916784.08327389.010.0002 Error16149.36666679.3354167 Corrected Total23737.9495833 R-SquareCoeff VarRoot MSEextol Mean 0.79759218.778333.05539116.27083 SourceDFType III SSMean SquareF ValuePr > F gender1176.5837500 18.920.0005 fat1242.5704167 25.980.0001 gender*fat113.6504167 1.460.2441 smoke170.3837500 7.540.0144 gender*smoke111.0704167 1.190.2923 fat*smoke172.4537500 7.760.0132 gender*fat*smoke11.8704167 0.200.6604

41 Exercise Example: Cell Means Level of gender Level of fat Level of smoke N extol MeanStd Dev 111325.96666672.81128678 112319.86666672.94844592 121314.06666671.56950098 122316.03333333.92470806 211319.83333332.15483951 212312.13333332.36290781 221312.06666673.57258077 222310.20000004.15090352

42 Exercise Example: Factor Effects Model ParameterEstimateStandard Errort ValuePr > |t| Intercept10.20000000B1.764031055.78<.0001 gender 15.83333333B2.494716642.340.0327 gender 20.00000000B... fat 11.93333333B2.494716640.770.4497 fat 20.00000000B... gender*fat 1 11.90000000B3.528062110.540.5976 gender*fat 1 20.00000000B... gender*fat 2 10.00000000B... gender*fat 2 20.00000000B... smoke 11.86666667B2.494716640.750.4652 smoke 20.00000000B... gender*smoke 1 1-3.83333333B3.52806211-1.090.2933 gender*smoke 1 20.00000000B... gender*smoke 2 10.00000000B... gender*smoke 2 20.00000000B... fat*smoke 1 15.83333333B3.528062111.650.1177 fat*smoke 1 20.00000000B... fat*smoke 2 10.00000000B... fat*smoke 2 20.00000000B... gender*fat*smoke 1 1 12.23333333B4.989433280.450.6604 gender*fat*smoke 1 1 20.00000000B... gender*fat*smoke 1 2 10.00000000B... gender*fat*smoke 1 2 20.00000000B... gender*fat*smoke 2 1 10.00000000B... gender*fat*smoke 2 1 20.00000000B... gender*fat*smoke 2 2 10.00000000B... gender*fat*smoke 2 2 20.00000000B...

43 Exercise Example: Factor Effects Model – conceptual constraints Obsgenderfatsmoke  111116.27082.71253.179171.7125 411216.27082.71253.17917-1.7125 712116.27082.7125-3.179171.7125 1012216.27082.7125-3.17917-1.7125 1321116.2708-2.71253.179171.7125 1621216.2708-2.71253.17917-1.7125 1922116.2708-2.7125-3.179171.7125 2222216.2708-2.7125-3.17917-1.7125 Obsgenderfatsmoke  11110.75417-0.679171.73750.27917 41120.754170.67917-1.7375-0.27917 7121-0.75417-0.67917-1.7375-0.27917 10122-0.754170.679171.73750.27917 13211-0.754170.679171.7375-0.27917 16212-0.75417-0.67917-1.73750.27917 192210.754170.67917-1.73750.27917 222220.75417-0.679171.7375-0.27917

44 Exercise Example: interaction plot of smoke vs. body fat title1 h=3 'Mean of smoke/fat vs. smoke'; symbol1 v=L i=join c=red; symbol2 v=H i=join c=black; proc gplot data=BCdat; plot muBC*smoke=fat /vaxis=axis2; run;

45 Exercise Example: Interaction Plot (cont)

46 Exercise Example: Reduced model data exercise; set exercise; fs = 10*fat + smoke; run; proc glm data=exercise; class gender fs; model extol=gender fs; means gender fs/tukey; run;

47 Exercise Example: Reduced model (cont) SourceDFSum of SquaresMean SquareF ValuePr > F Model4561.9916667140.497916715.17<.0001 Error19175.95791679.2609430 Corrected Total23737.9495833 R-SquareCoeff VarRoot MSEextol Mean 0.76155818.703283.04318016.27083 SourceDFType III SSMean SquareF ValuePr > F gender1176.5837500 19.070.0003 fs3385.4079167128.469305613.87<.0001

48 Exercise Example: Reduced model (cont) Means with the same letter are not significantly different. Tukey GroupingMeanNgender A18.983121 B13.558122 Means with the same letter are not significantly different. Tukey GroupingMeanNfs A22.900611 B16.000612 B B13.117622 B B13.067621

Download ppt "Hormone Example: nknw892.sas Y = change in growth rate after treatment Factor A = gender (male, female) Factor B = bone development level (severely depressed,"

Similar presentations

Statistical Methods Lynne Stokes Department of Statistical Science Lecture 7: Introduction to SAS Programming Language.

Statistical Methods Lynne Stokes Department of Statistical Science Lecture 7: Introduction to SAS Programming Language.
Topic 32: Two-Way Mixed Effects Model. Outline Two-way mixed models Three-way mixed models.

Topic 32: Two-Way Mixed Effects Model. Outline Two-way mixed models Three-way mixed models.
1 Chapter 4 Experiments with Blocking Factors. 2 4.1 The Randomized Complete Block Design Nuisance factor: a design factor that probably has an effect.

1 Chapter 4 Experiments with Blocking Factors The Randomized Complete Block Design Nuisance factor: a design factor that probably has an effect.
Analysis of covariance Experimental design and data analysis for biologists (Quinn & Keough, 2002) Environmental sampling and analysis.

Analysis of covariance Experimental design and data analysis for biologists (Quinn & Keough, 2002) Environmental sampling and analysis.
Topic 15: General Linear Tests and Extra Sum of Squares.

Topic 15: General Linear Tests and Extra Sum of Squares.
CS Example: General Linear Test (cs2.sas)

CS Example: General Linear Test (cs2.sas)
Partial Regression Plots. Life Insurance Example: (nknw364.sas) Y = the amount of life insurance for the 18 managers (in $1000) X 1 = average annual income.

Partial Regression Plots. Life Insurance Example: (nknw364.sas) Y = the amount of life insurance for the 18 managers (in $1000) X 1 = average annual income.
Matrix A matrix is a rectangular array of elements arranged in rows and columns Dimension of a matrix is r x c  r = c  square matrix  r = 1  (row)

Matrix A matrix is a rectangular array of elements arranged in rows and columns Dimension of a matrix is r x c  r = c  square matrix  r = 1  (row)
Be humble in our attribute, be loving and varying in our attitude, that is the way to live in heaven.

Be humble in our attribute, be loving and varying in our attitude, that is the way to live in heaven.
Multi-Factor Studies Stat 701 Lecture E. Pena.

Multi-Factor Studies Stat 701 Lecture E. Pena.
Mean Comparison With More Than Two Groups

Mean Comparison With More Than Two Groups
Two-Way ANOVA in SAS Multiple regression with two or

Two-Way ANOVA in SAS Multiple regression with two or
Example: Price Analysis for Diamond Rings in Singapore Variables Response Variable: Price in Singapore dollars (Y) Explanatory Variable: Weight of diamond.

Example: Price Analysis for Diamond Rings in Singapore Variables Response Variable: Price in Singapore dollars (Y) Explanatory Variable: Weight of diamond.
Incomplete Block Designs

Incomplete Block Designs
Distribution of X: (nknw096) data toluca; infile 'H:\CH01TA01.DAT'; input lotsize workhrs; seq=_n_; proc print data=toluca; run; Obslotsizeworkhrsseq 1803991.

Distribution of X: (nknw096) data toluca; infile 'H:\CH01TA01.DAT'; input lotsize workhrs; seq=_n_; proc print data=toluca; run; Obslotsizeworkhrsseq
Two-Way Balanced Independent Samples ANOVA Computations Contrasts Confidence Intervals.

Two-Way Balanced Independent Samples ANOVA Computations Contrasts Confidence Intervals.
SAS Lecture 5 – Some regression procedures Aidan McDermott, April 25, 2005.

SAS Lecture 5 – Some regression procedures Aidan McDermott, April 25, 2005.
Topic 16: Multicollinearity and Polynomial Regression.

Topic 16: Multicollinearity and Polynomial Regression.
Topic 28: Unequal Replication in Two-Way ANOVA. Outline Two-way ANOVA with unequal numbers of observations in the cells –Data and model –Regression approach.

Topic 28: Unequal Replication in Two-Way ANOVA. Outline Two-way ANOVA with unequal numbers of observations in the cells –Data and model –Regression approach.
1 Experimental Statistics - week 6 Chapter 15: Randomized Complete Block Design (15.3) Factorial Models (15.5)

1 Experimental Statistics - week 6 Chapter 15: Randomized Complete Block Design (15.3) Factorial Models (15.5)

Similar presentations

Ads by Google