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**STRIP PLOT DESIGN AND MULTILOCATIONS**

Erlina Ambarwati

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**SPLIT BLOCK DESIGN (STRIP PLOT)**

Utamanya digunakan dalam bidang pertanian. 2 faktor, A dan B, diacak pada main plot. A diacak dengan mendatar B diacak dengan vertikal Misalnya A: penggenangan B: penyemprotan herbisida A & B diconfoundedkan a1 a2 a4 a3 a0 b3 b2 b0 b1

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**Model Linear Tabel Anova SR df SS EMS (fixed treat) Blok A Error 1 B**

(r-1)(a-1) b-1 (r-1)(b-1) (a-1)(b-1) (r-1)(a-1)(b-1) SSR SSA SSE1 SSB SSE2 SSAB SSE3 Total rab-1 SStot 4/13/2017 Erlina Ambarwati

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**Penghitungan semua JK (SS) seperti pada simple split plot**

Penghitungan semua JK (SS) seperti pada simple split plot. Kecuali untuk errornya. 4/13/2017 Erlina Ambarwati

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**Konsentrasi Rep Genangan 1 2 3 4 5 6 I T S R 10,3 9,8 9,0 9,7 10,1 9,6**

11,2 11,0 10,8 10,4 10,5 10,6 9,9 9,5 184,1 II 11,8 10,7 11,6 10,9 12,1 11,9 12,3 11,7 9,2 200,3 10,2 9,3 9,4 185,3 91,1 92,3 102,7 97,1 96,2 90,3 569,7 4/13/2017 Erlina Ambarwati

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4/13/2017 Erlina Ambarwati

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**ANOVA SR df SS MS Fhit Ftab Rep. Kon. E1 Gen. E2 K*G E3 2 5 10 4 20**

4/13/2017 SR df SS MS Fhit Ftab Rep. Kon. E1 Gen. E2 K*G E3 2 5 10 4 20 9,05 12,19 2,81 3,30 0,90 4,45 3,44 4,52 2,44 0,28 1,15 0,23 0,44 0,17 8,71* 5ns 2,85ns 6,43 9,6 4,37 Total 52 36,17 Erlina Ambarwati PROC GLM; CLASS BLOCKS TREATS CROSS; MODEL WHATEVER = BLOCKS TREATS BLOCKS*TREATS CROSS CROSS*BLOCKS CROSS*TREATS; TEST H=BLOCKS TREATS E=BLOCKS*TREATS; TEST H=CROSS E=CROSS*BLOCKS; RUN;

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**The number of blocks is the number of replications.**

RCB repeated in time 4/13/2017 Field marks: Multiple measurements of the same experimental subjects are made in time. Treatments are assigned at random within blocks of adjacent subjects, each treatment once per block. The number of blocks is the number of replications. Erlina Ambarwati

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4/13/2017 this example. Erlina Ambarwati

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Layout 4/13/2017 First Block I A B C D E F Block II F A E B D C Block III C B F A D E Second Block I A B C D E F Third Block I A B C D E F Erlina Ambarwati

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**Anova RCBD Repeated time**

Source of variation Degrees of freedoma Sums of squares (SSQ) Mean square (MS) F Blocks (B) b-1 SSQB SSQB/(b-1) MSB/MSEm Treatment (Tr) t-1 SSQTr1 SSQTr/(t-1) MSTr/MSEm Error-main (Em) (b-1)*(t-1) SSQEm SSQEm/((b-1)*(t-1)) Time (Ti) (s-1) SSQTi SSQTi/(s-1) MSTi/MSE Time X Blocks (TxB) (s-1)*(b-1) SSQTxB SSQTxB/((s-1)*(b-1)) MSTxB/MSE Time X Treatments (TxT) (s-1)*(t-1) SSQTxT SSQTxT/((s-1)*(t-1)) MSTxT/MSE Error (E) (s-1)*(t-1)*(b-1) SSQE SSQE/((s-1)*(t-1)*(b-1)) Total (Tot) f*s*b-1 SSQTot awhere t=number of treatments, s=number of times measurements are taken, and b=number of blocks or replications. 4/13/2017 Erlina Ambarwati

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**Sample SAS GLM statements:**

4/13/2017 PROC GLM; CLASS BLOCKS TREAT; MODEL TIME1 TIME2 TIME3 = BLOCKS TREAT; REPEATED TIME /PRINTE; RUN; Erlina Ambarwati

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**RCB repeated at more than one location**

4/13/2017 Field marks: Blocks are laid out at more than one location. Treatments are assigned at random to those blocks as below. Treatments are assigned at random within blocks of adjacent subjects, each treatment once per block. The number of blocks is the number of replications. Any treatment can be adjacent to any other treatment, but not to the same treatment within the block. Erlina Ambarwati

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**Lay out multilocations**

4/13/2017 Erlina Ambarwati

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**Anova multilocations Source of variation Degrees of freedoma**

Sums of squares (SSQ) Mean square (MS) F Locations (L) l-1 SSQL SSQL/(l-1) MSL/MSEl Error for Locations (El) l*(b-1) SSQEl SSQEl/(l*(b-1)) Treatments (Tr) t-1 SSQTr SSQTr/(t-1) MSTr/MSE Treatments X Locations (TxL) (t-1)*(l-1) SSQTxL SSQTxL/((t-1)*(l-1)) MSTxL/MSE Error (E) l*(t-1)*(b-1) SSQE SSQE/(l*(t-1)*(b-1)) Total (Tot) l*t*b-1 SSQTot awhere l=number of locations, t=number of treatments and b=number of blocks or replications. 4/13/2017 Erlina Ambarwati

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**SAMPLE SAS GLM STATEMENTS:**

4/13/2017 PROC GLM; CLASS LOCS BLOCKS TREATS; MODEL WHATEVER = LOCS LOCS(BLOCKS) TREATS TREATS*LOCS ; TEST H = LOCS E = LOCS(BLOCKS); RUN; Erlina Ambarwati

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The Randomized Block Design. Suppose a researcher is interested in how several treatments affect a continuous response variable (Y). The treatments may.

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