Welcome to OPSTAT Home Page

1. Descriptive Statistics

• Generate Frequency Table: Summarizes the occurrence of values in a dataset.
• Cross Tabulation: Analyzes relationships between categorical variables.
• Comparing Means: Compares means across different groups using hypothesis testing.
• Correlation Analysis: Measures the strength and direction of relationships between variables.
• Regression Analysis: Models the relationship between dependent and independent variables.

2. Design of Experiments

• One Factor (CRD, RBD) Analysis: Analyzes single-factor experiments using CRD and RBD.
• Two Factors (CRD, RBD, Split-Plot) Analysis: Evaluates experiments with two factors.
• Three Factors (CRD, RBD, Split-Split Plot) Analysis: Supports three-factor experiments.
• Latin Square Design: Controls variability in single-treatment experiments.
• Strip Plot Design: Analyzes multiple factors in a structured layout.

3. Biometrical Methods

• Generation Means: Assesses mean performance in breeding studies.
• Path Analysis: Models relationships among variables.
• Diallel Analysis: Studies breeding value through genetic crosses.
• Stability Analysis: Evaluates the consistency of performance across environments.
• Augmented Designs: Facilitates analysis in unbalanced experiments.
• Balanced Lattice Design: Provides efficient designs for comparative trials.
• Triple Test Cross Analysis: Explores genetic inheritance through triple test crosses.

4. Multivariate Analysis

• Principal Component Analysis: Reduces dimensionality while preserving variance.
• K-Mean Cluster Analysis: Classifies data into distinct clusters.
• Correlation and Regression Analysis: Explores relationships among multiple variables.
• Probit Analysis: Models binary response data.
• Pooled Analysis: Combines data from different studies for comprehensive analysis.