Experimental Design Test 3

Each combination of the two factors applied at the same time is a cell pillar block( cell, pillar, block ) containing experimental units measurement units( experimental units, measurement units ).

If each cell in a two-factor CRD only contains one experimental unit, the calculations are the same as a Randomized Block ANOVA Repeated Time Measure ANOVA One-Way ANOVA( Randomized Block ANOVA, Repeated Time Measure ANOVA, One-Way ANOVA ), even though the designs are different.

This is an example of a/an Proportional Disproportional( Proportional, Disproportional ) Balanced Unbalanced( Balanced, Unbalanced ) design. In this design, n-cells are the same in all cells ratios of n-cells are the same ratios of n-cells are not the same( n-cells are the same in all cells, ratios of n-cells are the same, ratios of n-cells are not the same ) and tests of cells on diagonal are ok fail( are ok, fail ).

This is an example of a/an Proportional Disproportional( Proportional, Disproportional ) Unbalanced Balanced( Unbalanced, Balanced ) design. In this design, n-cells are the same in all cells ratios of n-cells are the same ratios of n-cells are not the same( n-cells are the same in all cells, ratios of n-cells are the same, ratios of n-cells are not the same ) and tests of cells on diagonal are ok fails( are ok, fails ).

This is an example of a/an Proportional Disproportional( Proportional, Disproportional ) Balanced Unbalanced( Balanced, Unbalanced ) design. In this design, n-cells are the same in all cells ratios of n-cells are not the same ratios of n-cells are the same( n-cells are the same in all cells, ratios of n-cells are not the same, ratios of n-cells are the same ) and tests of cells on diagonal are ok fail( are ok, fail ).

This is the results of an example Two-Factor CRD ANOVA with Multiple Replicates.

What kind of interaction is this? No Interaction Interaction due to Multiplicative Data Interaction due to biological process One might( No Interaction, Interaction due to Multiplicative Data, Interaction due to biological process, One might )
Can we interpret Main Effects? Yes No Yes, after a ln transformation( Yes, No, Yes, after a ln transformation )
Would Simple Effects be of interest? Possibly Definitely Likely not( Possibly, Definitely, Likely not )

This is the results of an example Two-Factor CRD ANOVA with Multiple Replicates.

What kind of interaction is this? No Interaction Interaction due to Multiplicative Data Interaction due to biological process( No Interaction, Interaction due to Multiplicative Data, Interaction due to biological process )
Can we interpret Main Effects? Yes No Yes, after a ln transformation One might( Yes, No, Yes, after a ln transformation, One might )
Would Simple Effects be of interest? Possibly Definitely Likely not( Possibly, Definitely, Likely not )

This is the results of an example Two-Factor CRD ANOVA with Multiple Replicates.

What kind of interaction is this? No Interaction Interaction due to Multiplicative Data Interaction due to biological process( No Interaction, Interaction due to Multiplicative Data, Interaction due to biological process )
Can we interpret Main Effects? Yes No Yes, after a ln transformation One might( Yes, No, Yes, after a ln transformation, One might )
Would Simple Effects be of interest? Possibly Definitely Likely not( Possibly, Definitely, Likely not )

This is the results of an example Two-Factor CRD ANOVA with Multiple Replicates.

What kind of interaction is this? No Interaction Interaction due to Multiplicative Data Interaction due to biological process( No Interaction, Interaction due to Multiplicative Data, Interaction due to biological process )
Can we interpret Main Effects? Yes No Yes, after a ln transformation One might( Yes, No, Yes, after a ln transformation, One might )
Would Simple Effects be of interest? Possibly Definitely Likely not( Possibly, Definitely, Likely not )

Procedure for Aligned Rank Transform for a Two-Factor ANOVA

1. Align Rank Average( Align, Rank, Average ) data seperately al together( seperately, al together ) producing three five seven one( three, five, seven, one ) different data sets
2. Rank aligned Align ranked Average ranked Average aligned Align averaged Rank averaged( Rank aligned, Align ranked, Average ranked, Average aligned, Align averaged, Rank averaged ) data all together seperately( all together, seperately ) within between( within, between ) each of the three one five seven( three, one, five, seven ) data sets
3. Replace original data with ranks average alignments( ranks, average, alignments ) within between( within, between ) each of the three one five seven( three, one, five, seven ) data sets
4. Run three one five seven( three, one, five, seven ) separate combined( separate, combined ) Two-Factor One-Factor Three-Factor( Two-Factor, One-Factor, Three-Factor ) ANOVAs on ranks averages alignments( ranks, averages, alignments )

Procedure for Aligned Rank Transform for a Three-Factor ANOVA

1. Align Rank Average( Align, Rank, Average ) data seperately all together( seperately, all together ) producing three one five seven( three, one, five, seven ) different data sets
2. Rank aligned Align ranked Rank averaged Align averaged Average ranked Average aligned( Rank aligned, Align ranked, Rank averaged, Align averaged, Average ranked, Average aligned ) data all together separately( all together, separately ) within between( within, between ) each of the three one five seven( three, one, five, seven ) data sets
3. Replace original data with ranks averages alignments( ranks, averages, alignments ) within between( within, between ) each of the three seven five one( three, seven, five, one ) data sets
4. Run three one five seven( three, one, five, seven ) separate combined( separate, combined ) Two-Factor One-Factor Three-Factor( Two-Factor, One-Factor, Three-Factor ) ANOVAs on ranks averages alignments( ranks, averages, alignments )

Linear regrassion equations describe the relationship beetween one or more independent dependent( independent, dependent ) quantitative qualitative( quantitative, qualitative ) variables and a dependent independent( dependent, independent ) variable.

The OLS method aims to minimize the sum of sqaure differences sum of differences average difference average error sum of error terms( sum of sqaure differences, sum of differences, average difference, average error, sum of error terms ) between the observed and predicted values.

Steps of inverse prediction:

1. Measure the dependent independent( dependent, independent ) variable (y x( y, x )) at known values of the independent dependent( independent, dependent ) variable (x y( x, y )).
2. Use these values to create a standard curve line of correlation central axis( standard curve, line of correlation, central axis ) and find the regression equation
3. Rearrange the regression, isolating x y( x, y ) on one side
4. Measure the unknown, finding the y-value x-value( y-value, x-value )
5. Plug the measurement into the rearranged equation to find the unknown value

Interpolation Extrapolation( Interpolation, Extrapolation ): using the section of the OLS line bounded by the dataset for data prediction (good use of equation not recommended( good use of equation, not recommended ))

Extrapolation Interpolation( Extrapolation, Interpolation ): using sections of the OLS line not bounded by the dataset in order to complete data prediction (not recommended good use of equation( not recommended, good use of equation ))

In ANCOVA, for each individual EU/MU block factor( EU/MU, block, factor ) a covariate an additional factor an additional level a block( a covariate, an additional factor, an additional level, a block ) is measured to account for variation other than the treatment effect.

In a t-test of the correlation coefficient, the null hypothesis is r = rho = 0 r^2 = rho = 0 r = rho <> 0 r^2 = rho <> 0 r1 = r2( r = rho = 0, r^2 = rho = 0, r = rho <> 0, r^2 = rho <> 0, r1 = r2 ). For a two-tailed test, t follows the t Durbin-Watson r x^2 G( t, Durbin-Watson, r, x^2, G )-distribution with v degrees of freedom.

Chi-squared test uses x^2 t G D z( x^2, t, G, D, z ) statistic, which follows the x^2 t G D z( x^2, t, G, D, z ) distribution table with v degrees of freedom.

Log-likelihood test uses the G x^2 t D z( G, x^2, t, D, z ) statistic, which follows the x^2 G D t z( x^2, G, D, t, z ) distribution table with v degrees of freedom.

Heterogeneitiy Log-Likelihood Tests analyze correlation linear regression( correlation, linear regression ) by combining Goodness of Fit Contingency Table ANOVA ANCOVA( Goodness of Fit, Contingency Table, ANOVA, ANCOVA ) tests.

If the heterogeneity G test does not reject the null hypothesis of homogeneity across all tests, the Pooled G Chi-squared Log-likelihood Durbin-Watson( Pooled G, Chi-squared, Log-likelihood, Durbin-Watson ) test is a legitimate test of adding the counts of all tests together for a combined test with a larger number of counts.