Pooled Analysis

A pooled analysis is a stastical technique for combining the results of multiple epidemiological studies. It is often used when the results of individual studies do not allow for a firm conclusion to be drawn. Unlike meta-analyses, pooled analyses can only be conducted if the included studies used the same study design and statistical models, and if their respective populations were homogeneous. If individual-level data from the included studies is available, the result of a pooled analysis can be considered more reliable.

Pooled Analysis:

• Maximum 30 entries for single trait can be analysed

•  Single trait over 16 locations are analysed.

• Result  sheet can be downloaded and  saved in your selected  folder

Stability Analysis

  Stability refers to the performance of Genotypes interaction with respective to environmental factors overtime within given location. Selection for stability is not possible until a biometrical model with suitable parameters is available to provide criteria necessary to rank varieties / breeds for stability.

Stability Analysis:

• Maximum 30 entries for single trait can be analysed

•  Single trait over 12 environments/locations are analysed.

• Result  sheet can be downloaded and  saved in your selected  folder

Factorial RBD Analysis

A Factorial experiment is a basic RBD design is an experiment in which more than one factor is tried and uses RBD as the experimental design. This design was chosen because the experimental units used were not uniform, so they needed grouping. This designs permit the researcher to determine the effect of more than one independent variable on a dependent variable and to determine the possible interaction of multiple independent variables. That is, the effect one independent variable may differ across different levels of another independent variable.

Factorial experiments involve simultaneously more than one factor and each factor is at two or more levels. Several factors affect simultaneously the characteristic under study in factorial experiments and the experimenter is interested in the main effects and the interaction effects among different factors.

Factorial Analysis:

• This has a capacity to  analyze six replications and two factors each up to six levels.

• This will give ANOVA, SEm, CD, C.V. and Mean tables. 

• Result  sheet can be downloaded and  saved in your selected  folder

Split Plot Design -Analysis 

A split-plot design is characterized by separate random assignments of levels of factors, where levels of some factors are assigned to larger experimental units called whole plots. Each whole (Main) plot is divided into smaller (sub plot) units, called split-plots, and levels of another factor are randomly assigned to split-plots. It is used when some factors are harder (or more expensive) to vary than others. Basically a split plot design consists of two experiments with different experimental units of different “size”. experimental units, whereas other factors can be easily applied to “smaller” plots of land. A split-plot design leads to an increase in precision in the estimates for all factor effects except for the whole-plot main effects. 

Split Plot Expt. Design Analysis:

• This has a capacity to  analyze six main plots and six sub plot, with six replications

• This will give ANOVA, SEm, CD, C.V. and Mean tables. 

• Result  sheet can be downloaded and  saved in your selected  folder

Latin Square Design -Analysis 

A latin square is a design in which each treatment is assigned to each time period the same number of times and to each subject the same number of times. If there are t treatments, t time periods, and mt subjects then m latin squares (each with t treatment sequences) would be used.

Latin square designs are particularly useful in agricultural settings where multiple gradients may occur. The LS requires the number of treatments to be equal to the number of rows and the number of columns. Thus, trials with few treatments (four to eight) lend themselves well to a LS design. 

The Latin square design applies when there are repeated exposures/treatments and two other factors. This design avoids the excessive numbers required for full three way ANOVA.  

Latin Square Expt. Design Analysis:

• This has a capacity to  analyze 5 columns and 5 Rows (5x5)

• This will give ANOVA, SEm, CD, C.V. and Mean tables. 

• Result  sheet can be downloaded and  saved in your selected  folder