Compare the computed F value of step 4 with the tabular F values of step 5, and decide on the significance of the differences among treatments. The sum of squares you want to use to test your hypothesis will be based on the adjusted treatment sum of squares, \(R( \tau_i | \mu, \beta_j) \) using the notation for testing:The numerator of the F-test, for the hypothesis you want to test, should be based on the adjusted SS’s that is last in the sequence or is obtained from the adjusted sums of squares. TreatmentRS 1RS 2RS 3RS 4RS 5RS 10. Comparison of treatmentsThe treatment means are compared as illustrated for the case of CRD in Section 4.
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If a quadratic effect is expected for a factor, a more complicated experiment should be used, such as a central composite design. . The test results for all pairs of treatments are given in Table 4. Through randomization, every experimental unit will have the same chance of receiving any treatment. 35625.
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Such a large experiment would mean difficulties go to this site financing, in obtaining an adequate experimental area, in controlling soil heterogeneity, and so on. 10. That will be very close to what you would get using the approximate method we mentioned earlier. 6
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30. The cv varies greatly with the type of experiment, the crop grown, and the characters measured. The procedure is illustrated for the case of a field experiment with six treatments A, B, C, D, E, F and three replications.
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27SST = = 762. So you can analyze the resulting data, but now This Site reduce your error degrees of freedom by one. 4. 97103.
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If both values are large, the trial may be repeated and efforts made to reduce the experimental error so that the differences among treatments, if any, can be detected. Mean maximum culm height of Bambusa arundinacea tested with three age levels and two levels of spacing in a RCBD. Compare difference among the treatment means against the computed value of LSD and place the asterisk against significant differences. What you also want to notice is the standard error of these means, i. Should we test the block factor?Below is the Minitab output which treats both batch and treatment the same and tests the hypothesis of no effect.
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94. The researcher wants to compare the five isolates of R. 4335. You do not have as many data points on that particular treatment. 17)where s2 is the mean square due to error and r is the number of replications. Source of variationDegree of freedomSum of squares Mean squareComputed FTabular F 5%Treatment 4762.
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3826. Besides the variations produced in the observations due to these known sources, the variations are also produced by a large number of unknown sources such as uncontrolled variation in extraneous factors related to the environment, genetic variations in the experimental material other than that due to treatments, etc. 80 1. Table 4.
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9 To compute the main effect of a factor “A”, subtract the average response of all experimental runs for which A was at its low (or first) level from the average response of all experimental runs for which A was at its high (or second) level. 2065. 37
55. 5 with one missing observation
Treatment (Provenance)
find out here Replication
Treatment total
Rep.
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