MST-025 IGNOU Guess Paper 2026-27
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Syllabus & Overview
MST-025 (Categorical and Survival Analysis) TEE Guess Paper (Digital PDF)
This structured guess paper aligns with the official IGNOU curriculum for MST-025, covering key syllabus blocks and question patterns from the last 5–10 Term-End Exams (June & December sessions). It prioritizes high-scoring topics, time management, and solved numerical examples to optimize preparation.
Block-1: Categorical Data Analysis
This block accounts for 30–35% of the total marks. Focus on:
- Logistic Regression: Model fitting, odds ratio interpretation, and goodness-of-fit tests (e.g., Hosmer-Lemeshow). Previous papers frequently test logistic regression for binary outcomes.
- Chi-Square Tests: Independence tests, homogeneity tests, and likelihood ratio tests. Expect 2–3 numerical questions on contingency tables.
- Multinomial Logistic Regression: Extensions for ordinal and nominal responses. Solve at least 2 problems from past papers.
Block-2: Survival Analysis
This block carries 40–45% of the marks. Prioritize:
- Kaplan-Meier Estimator: Survival curves, median survival time, and log-rank test. Past papers often include 3–4 questions on this topic.
- Cox Proportional Hazards Model: Interpretation of hazard ratios, partial likelihood, and model diagnostics. Focus on numerical applications.
- Competing Risks: Cumulative incidence function and subdistribution hazards. Less common but high-weightage in recent exams.
Exam Strategy & Time Management
- Allocate 60 minutes for Block-1 (Categorical Data) and 90 minutes for Block-2 (Survival Analysis). Leave 30 minutes for review.
- Prioritize numerical questions (60–70% of marks). Practice at least 5–6 problems per topic from past papers.
- For survival analysis, memorize key formulas (e.g., Kaplan-Meier estimator, log-rank test statistic) and apply them directly to given data.
FAQs
Q1: Are there any mandatory derivations in the exam?No. The exam primarily tests application (e.g., fitting models, interpreting results) rather than theoretical derivations. Focus on numerical problems and interpretations.
Q2: How should I approach mixed questions (e.g., combining logistic regression with survival analysis)?Break them into smaller steps. For example, if a question links logistic regression to survival data, first analyze the categorical outcome (Block-1) and then extend to survival analysis (Block-2). Use past papers as references.
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