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Re: st: Re: ANOVA
From
Wendy Alfaro <[email protected]>
To
[email protected]
Subject
Re: st: Re: ANOVA
Date
Thu, 6 Mar 2014 19:05:53 -0600
Thank you so much.
My study is about measuring some variables (texture, color, etc) in
muffins made out of gluten free flour. I use four treatments and three
repetitions for each treatment.
Thank you,
On Thu, Mar 6, 2014 at 6:56 PM, Joseph Coveney <[email protected]> wrote:
> Wendy Alfaro wrote:
>
> I need to run and Anova with these data. How do I perform the test with Stata?
>
> Dureza primer mordisco
> Tratamiento (Sust. Huevo)% Muestra 1 Muestra 2 Muestra 3
> 1.1 1.2 1.3 2.1 2.2 2.3 3.1 3.2 3.3
> 100% 29,7562 35,0566 32,8892 19,9594 28,9730 22,3698 37,4985 31,2923 31,8955
> 75% 22,3752 27,6344 30,8096 22,0975 22,4783 28,2321 27,9858 25,2717 21,7764
> 50% 25,9648 29,3983 26.9672 30,4386 27,0703 31,4941 43,7090 62,9589 44,2992
> 0% 24,8714 24,0784 28,6552 33,1658 33,7440 33,9544 20,8814 24,6869 19,3974
> I need to run and Anova with these data. How do I perform the test with Stata?
>
> Dureza segundo mordisco
> Tratamiento (Sust. Huevo)% Muestra 1 Muestra 2 Muestra 3
> 1.1 1.2 1.3 2.1 2.2 2.3 3.1 3.2 3.3
> 100% 20,0689 25,8021 21,6245 15,8371 21,7363 17,6238 29,5198 25,1263 25,4528
> 75% 15,3175 19,0080 22,3080 17,3005 19,1132 20,8489 23,4535 21,6940 19,2889
> 50% 18,8561 22,0628 19,1392 22,9024 21,7797 26,4107 34,7594 50,0161 36,8400
> 0% 19,5395 17,8472 23,7215 28,3482 27,5487 28,6693 18,2164 20,9888 16,1398
>
> Dureza, pico negativo de elasticidad del primer pico
> Tratamiento (Sust. Huevo)% Muestra 1 Muestra 2 Muestra 3
> 1.1 1.2 1.3 2.1 2.2 2.3 3.1 3.2 3.3
> 100% -0,0409 -0,0499 -0,1909 -0,0835 -0,1052 -0,0282 -0,1182 -0,1367 -0,0488
> 75% -0,0824 -0,1410 -0,0184 -0,0846 -0,0239 -0,0998 0,0033 -0,0759 0,0141
> 50% -0,0358 -0,0955 -0,0108 -0,0054 -0,0184 0,0011 -0,0098 -0,0749 -0,0098
> 0% -0,0228 -0,0152 -0,0011 -0,0033 -0,0521 0 -0,0054 0,0087 0
>
> Dureza, pico negativo de elasticidad del segundo pico
> Tratamiento (Sust. Huevo)% Muestra 1 Muestra 2 Muestra 3
> 1.1 1.2 1.3 2.1 2.2 2.3 3.1 3.2 3.3
> 100% -0,1258 -0,0911 -0,0949 -0,0879 -0,0868 -0,0358 -0,1345 -0,0868 -0,1009
> 75% -0,0683 -0,0087 -0,0477 -0,0792 -0,0184 -0,1041 -0,0759 -0,0694 -0,0629
> 50% -0,0250 -0,1041 -0,0065 0 -0,0022 -0,0597 -0,0477 -0,0239 -0,0184
> 0% -0,0011 -0,0119 -0,0466 -0,0553 0,0499 -0,0510 0,0065 -0,0304 -0,0108
>
> --------------------------------------------------------------------------------
>
> Try something like that below. I show the MANCOVA table beneath the do-file.
>
> I have no idea what your study is all about (for example, for the sake of data
> input, es. mordisco => Eng. bite), or whether the pair of paired datasets is
> supposed to represent two outcome variables for common explanatory variables.
> But MANCOVA is always more fun to me than ANOVA, so that's how I set it up.
>
> Joseph Coveney
>
> clear *
> set more off
> // Nota bene: "hard-coded" correction of data-entry error: 26.9672 -> 26,9672
> quietly input str244 a1-a30
> 100% 29,7562 35,0566 32,8892 19,9594 28,9730 22,3698 37,4985 31,2923 31,8955
> 75% 22,3752 27,6344 30,8096 22,0975 22,4783 28,2321 27,9858 25,2717 21,7764
> 50% 25,9648 29,3983 26,9672 30,4386 27,0703 31,4941 43,7090 62,9589 44,2992
> 0% 24,8714 24,0784 28,6552 33,1658 33,7440 33,9544 20,8814 24,6869 19,3974
> end
>
> generate byte bite = 1
> tempfile tmpfil0
> quietly save `tmpfil0'
>
> drop _all
> quietly input str244 a1-a30
> 100% 20,0689 25,8021 21,6245 15,8371 21,7363 17,6238 29,5198 25,1263 25,4528
> 75% 15,3175 19,0080 22,3080 17,3005 19,1132 20,8489 23,4535 21,6940 19,2889
> 50% 18,8561 22,0628 19,1392 22,9024 21,7797 26,4107 34,7594 50,0161 36,8400
> 0% 19,5395 17,8472 23,7215 28,3482 27,5487 28,6693 18,2164 20,9888 16,1398
> end
>
> generate byte bite = 2
> append using `tmpfil0'
> generate long h11 = real(a3 + a5)
> generate long h12 = real(a6 + a8)
> generate long h13 = real(a9 + a11)
> generate long h21 = real(a12 + a14)
> generate long h22 = real(a15 + a17)
> generate long h23 = real(a18 + a20)
> generate long h31 = real(a21 + a23)
> generate long h32 = real(a24 + a26)
> generate long h33 = real(a27 + a29)
> destring a1, generate(trt)
> drop a*
> quietly save `tmpfil0', replace
>
> drop _all
> // Nota bene: "hard-coded" correction of data format inconsistency
> quietly input str244 a1-a30
> 100% -0,0409 -0,0499 -0,1909 -0,0835 -0,1052 -0,0282 -0,1182 -0,1367 -0,0488
> 75% -0,0824 -0,1410 -0,0184 -0,0846 -0,0239 -0,0998 0,0033 -0,0759 0,0141
> 50% -0,0358 -0,0955 -0,0108 -0,0054 -0,0184 0,0011 -0,0098 -0,0749 -0,0098
> 0% -0,0228 -0,0152 -0,0011 -0,0033 -0,0521 0,0000 -0,0054 0,0087 0,0000
> end
>
> generate byte bite = 1
> tempfile tmpfil1
> quietly save `tmpfil1'
>
> drop _all
> // Nota bene: "hard-coded" correction of data format inconsistency
> quietly input str244 a1-a30
> 100% -0,1258 -0,0911 -0,0949 -0,0879 -0,0868 -0,0358 -0,1345 -0,0868 -0,1009
> 75% -0,0683 -0,0087 -0,0477 -0,0792 -0,0184 -0,1041 -0,0759 -0,0694 -0,0629
> 50% -0,0250 -0,1041 -0,0065 0,0000 -0,0022 -0,0597 -0,0477 -0,0239 -0,0184
> 0% -0,0011 -0,0119 -0,0466 -0,0553 0,0499 -0,0510 0,0065 -0,0304 -0,0108
> end
>
> generate byte bite = 2
> append using `tmpfil1'
> generate long enp11 = real(a3 + a5)
> generate long enp12 = real(a6 + a8)
> generate long enp13 = real(a9 + a11)
> generate long enp21 = real(a12 + a14)
> generate long enp22 = real(a15 + a17)
> generate long enp23 = real(a18 + a20)
> generate long enp31 = real(a21 + a23)
> generate long enp32 = real(a24 + a26)
> generate long enp33 = real(a27 + a29)
> destring a1, generate(trt)
> drop a*
>
> merge 1:1 trt bite using `tmpfil0', assert(match) nogenerate noreport
> quietly reshape long enp1 enp2 enp3 h1 h2 h3, i(trt bite) j(reading)
> quietly reshape long enp h, i(trt bite reading) j(sample)
> rename h hard
> manova enp hard = trt bite trt#bite / sample|trt#bite ///
> reading trt#reading bite#reading trt#bite#reading
>
> exit
>
> MANCOVA table, copied and pasted from Results Window:
>
> Number of obs = 72
>
> W = Wilks' lambda L = Lawley-Hotelling trace
> P = Pillai's trace R = Roy's largest root
>
> Source | Statistic df F(df1, df2) = F Prob>F
> -----------------+--------------------------------------------------
> Model | W 0.0402 39 78.0 62.0 3.17 0.0000 e
> | P 1.5274 78.0 64.0 2.65 0.0000 a
> | L 9.7576 78.0 60.0 3.75 0.0000 a
> | R 7.9899 39.0 32.0 6.56 0.0000 u
> |--------------------------------------------------
> Residual | 32
> -----------------+--------------------------------------------------
> trt | W 0.1393 3 6.0 30.0 8.40 0.0000 e
> | P 1.0668 6.0 32.0 6.10 0.0002 a
> | L 4.7005 6.0 28.0 10.97 0.0000 a
> | R 4.3612 3.0 16.0 23.26 0.0000 u
> |--------------------------------------------------
> bite | W 0.7214 1 2.0 15.0 2.90 0.0864 e
> | P 0.2786 2.0 15.0 2.90 0.0864 e
> | L 0.3861 2.0 15.0 2.90 0.0864 e
> | R 0.3861 2.0 15.0 2.90 0.0864 e
> |--------------------------------------------------
> trt#bite | W 0.9862 3 6.0 30.0 0.03 0.9998 e
> | P 0.0139 6.0 32.0 0.04 0.9997 a
> | L 0.0140 6.0 28.0 0.03 0.9998 a
> | R 0.0121 3.0 16.0 0.06 0.9779 u
> |--------------------------------------------------
> sample|trt#bite | 16
> -----------------+--------------------------------------------------
> reading | W 0.8531 2 4.0 62.0 1.28 0.2869 e
> | P 0.1493 4.0 64.0 1.29 0.2831 a
> | L 0.1694 4.0 60.0 1.27 0.2915 a
> | R 0.1510 2.0 32.0 2.42 0.1053 u
> |--------------------------------------------------
> trt#reading | W 0.8122 6 12.0 62.0 0.57 0.8607 e
> | P 0.1917 12.0 64.0 0.57 0.8616 a
> | L 0.2264 12.0 60.0 0.57 0.8606 a
> | R 0.2025 6.0 32.0 1.08 0.3950 u
> |--------------------------------------------------
> bite#reading | W 0.8767 2 4.0 62.0 1.05 0.3868 e
> | P 0.1233 4.0 64.0 1.05 0.3880 a
> | L 0.1406 4.0 60.0 1.05 0.3868 a
> | R 0.1406 2.0 32.0 2.25 0.1218 u
> |--------------------------------------------------
> trt#bite#reading | W 0.9159 6 12.0 62.0 0.23 0.9960 e
> | P 0.0846 12.0 64.0 0.24 0.9957 a
> | L 0.0914 12.0 60.0 0.23 0.9962 a
> | R 0.0859 6.0 32.0 0.46 0.8339 u
> |--------------------------------------------------
> Residual | 32
> -----------------+--------------------------------------------------
> Total | 71
> --------------------------------------------------------------------
> e = exact, a = approximate, u = upper bound on F
>
> .
> . exit
>
> end of do-file
>
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
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> * http://www.ats.ucla.edu/stat/stata/
--
Wendy Gabriela Alfaro Chaves
Consultora e investigadora
Desarrollo sostenible
Tel: (506) 2494-3647
Correo-e: [email protected]
Skype: wendy07
*
* For searches and help try:
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* http://www.stata.com/support/faqs/resources/statalist-faq/
* http://www.ats.ucla.edu/stat/stata/