MST-016 IGNOU Solved Assignment 2026-27

MST-016 Solved Assignment Statistical Inference (TMA Solutions)

This verified digital PDF provides mandatory 30% course weightage solutions for MST-016 Statistical Inference, structured strictly per IGNOU’s official curriculum for the M.Sc. (Applied Statistics) program. All responses comply with academic session word limits (500/250/100 words) and include plagiarism-free derivations for theoretical and numerical questions.

Key Syllabus Blocks Covered

• Block-1: Fundamentals of Statistical Inference
  • Definition and scope of statistical inference in natural sciences.
  • Distinction between population parameters and sample statistics.
  • Role of probability distributions (binomial, Poisson, normal) in inference.
• Block-2: Properties of Good Estimators
  • Unbiasedness, consistency, and efficiency of estimators (MLE, MME).
  • Comparison of estimators using variance and mean squared error.
  • Applications in physical sciences (e.g., estimating population mean).
• Block-3: Methods of Estimation
  • Maximum Likelihood Estimation (MLE) with derivations for exponential family.
  • Method of Moments (MME) for discrete and continuous distributions.
  • Bayesian estimation with prior/posterior distributions (life sciences examples).
• Block-4: Testing of Hypothesis (Parametric Tests)
  • Z-test, t-test, and chi-square tests with null/alternative hypotheses.
  • Type I/II errors and power of a test in experimental design.
  • Practical examples from physical sciences (e.g., testing variance equality).

Sample FAQs

Q1: How are MLE and MME different in practice? A: MLE maximizes the likelihood function to estimate parameters, often yielding asymptotically efficient estimators, while MME equates sample moments to population moments, which is simpler but may lack precision for skewed distributions. Q2: What is the role of the t-distribution in hypothesis testing? A: