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Analysis of Data from Designed Experiments

Incomplete Block Design


Example : An experiment was conducted at Jorhat under the aegis of Project Directorate of Cropping Systems Research, Modipuram using a balance incomplete block (BIB) design with parameters v = b = 7, r = k = 4, λ = 2.  The treatment details are as given below:


Treatments (Crop Sequences)

Season T1 T2 T3 T4 T5 T6 T7
Kharif  Rice Rice Rice Rice Rice Rice Rice
Rabi - Boro Rice Mustard  Brinjal Tomato French Bean Potato
Summer Rice - Rice Rice Rice Rice Rice

The yields from each of the crop sequence were converted into calories/ hectare. The block structure and the calories of the output per hectare obtained are given as

Block 1 T2 (3325060) T4 (2606200) T5 (3279420) T6 (2330180)
Block 2 T1 (2992900) T2 (3228180) T5 (3348780) T7 (2982000)
Block 3 T1 (3639920) T4 (2467800) T6 (2196580) T7 (2730780)
Block 4 T3 (2602410) T5 (3696340) T6 (2388060) T7 (2921790)
Block 5 T1 (3055180) T3 (2653680) T4 (2501060) T5 (3594320)
Block 6 T2 (3380420) T3 (2760690) T4 (2522100) T7 (2961270)
Block 7 T1 (2921420) T2 (3380420) T3 (2677400) T6 (2594420)
T# denotes the treatment number and the figures in brackets are the Kilo calories/hectare.



1. Perform the analysis of variance of the data to test whether there is any difference between treatments.

2. Perform the analysis of variance of the data to test whether there is any difference between blocks.

3. Obtain adjusted treatment means and perform all possible pair wise treatment comparisons.

4. The experimenter is interested in ascertaining  that the average effect of 1st two treatments is same as the average effect of the remaining 5 treatments.

5. Also compare the treatment 3,4,5,6,7 among themselves.

 Analysis Using SAS                                                         Analysis Using SPSS                                     





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Descriptive Statistics
Tests of Significance
Correlation and Regression
Completely Randomised Design
RCB Design
Incomplete Block Design
Resolvable Block Design
Augmented Design
Latin Square Design
Factorial RCB Design
Partially Confounded Design
Factorial Experiment with Extra Treatments
Split Plot Design
Strip Plot Design
Response Surface Design
Cross Over Design
Analysis of Covariance
Diagnostics and Remedial Measures
Principal Component Analysis
Cluster Analysis
Groups of Experiments
Non-Linear Models
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Other Designed Experiments
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For exposure on SAS, SPSS, MINITAB, SYSTAT and  
MS-EXCEL for analysis of data from designed experiments:

 Please see Module I of Electronic Book II: Advances in Data Analytical Techniques

available at Design Resources Server(