Skip to content
CampusPrep
AD-703 (C) · Advanced Statistical Analytics/Official Syllabus

Advanced Statistical Analytics (AD-703 (C)) - Official Syllabus

1
Unit 1

Introduction: Population and Sample

  • Population and Sample
  • Random Sampling from finite population (SRSWR and SRSWOR)
  • Parameter and Statistic
  • Sampling distribution of a statistic in the context of a finite population
  • Sampling distribution of sample mean and sample proportion while sampling from a finite population
  • Random sampling from an infinite population
  • Sampling Distribution of sample mean and sample variance when the sample is drawn from a Normal distribution
  • Problems on sampling distributions of statistics from finite and infinite populations
  • Statement of Lyndeberg-Levy Central Limit Theorem (CLT) and its applications
2
Unit 2

Correlation, Regression Analysis and ANOVA

  • Correlation, Scatter diagram
  • Karl Pearson's coefficient of correlation
  • Spearman's Rank correlation coefficient
  • Methods of least square
  • Simple linear Regression model, SLR assumptions and prediction
  • Multiple linear Regression, MLR assumption and prediction
  • Polynomial Regression
  • Logistics Regression
  • Poisson Regression
  • Non-Linear Regression
  • Analysis of Variance (One way & Two Way)
  • Analysis of Covariance
  • Multivariate Analysis of Variance
3
Unit 3

Testing of Hypothesis

  • Null and Alternative Hypothesis
  • Testing Procedure (Critical region)
  • Type I and Type II errors
  • Level of significance & Power of a test, p-value for symmetric null distribution
  • Tests for mean and proportion (single sample, two sample; exact & large sample)
  • Tests for variance (single sample and two samples)
  • Tests for mean and correlation coefficient for paired sample (Exact & Large sample)
  • Analysis of Variance (one way)
4
Unit 4

Parametric Point Estimation

  • Problem of point estimation
  • Criteria of a good Estimator
  • Unbiasedness
  • Consistency
  • Efficiency, Sufficiency Minimum Variance and Unbiasedness (Small sample)
  • Method of moments
  • Method of Maximum Likelihood
  • Consistency & Efficiency (Large sample)
  • Interval Estimation: Confidence Intervals of mean and proportion in large samples
5
Unit 5

Bayesian Statistics

  • Introduction to Bayesian inference
  • Bayesian parameter estimation
  • Markov Chain Monte Carlo (MCMC) methods
  • Bayesian hierarchical models
  • Survival analysis
  • Causal inference
  • High-dimensional data analysis

Reference Books

  • Understanding and using Advance Statistics — Jeremy Foster, Emma Barkus, Christian Yavorsky
  • Understanding Advanced Statistical Methods — Peter Westfall, Kevin S. S. Henning
  • Statistical Methods — S.P. Gupta
  • Mathematical Statistics — S.C. Gupta, V.K. Kapoor
Go to where you left off?

Quick Add to Notes

Save questions, your own notes and screenshots into notes filed by unit. It takes a free account.

Create free account

Have an account? Log in