STATISTICAL INFERENCE AND SAMPLING

Paper Code: 
CBDA 316
Credits: 
3
Periods/week: 
3
Max. Marks: 
100.00
Objective: 

The course will enable the students to

1. Understand the concepts of statistical inference.

2.  Learn the concepts of sampling.

 

Course Outcomes (COs).

 Course outcome (at course level)

Learning and teaching strategies

Assessment Strategies 

On completion of this course, the students will:

CO131. Attain theoretical knowledge about fundamental principles for statistical inference.

CO132. Perform point estimation and interval estimation under a variety of discrete and continuous probability models.

CO133. Apply the applications of sampling distributions to the real-world problems.

CO134. Conduct hypothesis test about population mean and proportion.

CO135. Analyse and conduct sample surveys by using an appropriate Sampling Technique.

Approach in teaching:

Interactive Lectures, Group Discussion, Tutorials, Case Study

 

Learning activities for the students:

Self-learning assignments, Machine Learning exercises, presentations

Class test, Semester end examinations, Quiz, Practical Assignments, Presentation

 

9.00
Unit I: 

Point estimation and Interval Estimation, properties of a good point estimator- unbiasedness, consistency, efficiency & sufficiency.-factorization theorem (without proof) and its applications.

 

9.00
Unit II: 

 Method of Maximum Likelihood and its properties of MLEs (without proof). Confidence interval, confidence coefficient, construction of confidence interval for population mean, variance, difference of population mean when standard deviation are known and unknown of Normal Distribution.

 

9.00
Unit III: 

Hypothesis and procedure of testing. Applications of Chi-square test, t-test and F –test. ANOVA: one way and two way

 

9.00
Unit IV: 

Central limit theorem. Sampling for attributes and variables, tests of significance for single mean, standard deviation and proportions, tests of significance for difference between two means, standard deviations and proportions for large samples.

 

9.00
Unit V: 

Principles of Sample survey, Probability and Non- probability Sampling, Concept of Sampling Design Method of drawing a random sample from a finite population, accuracy and precision of an estimator. Estimation of sample size for a specified precision

 

ESSENTIAL READINGS: 
  1. Goon, A.M., Gupta, M.K. and Dasgupta, B. Das (1991): An Outline of Statistics, Volume II, The World Press Pvt Ltd, Calcutta
  2. Gupta, S.C. and Kapoor, V.K.(2000): Fundamentals of Mathematical Statistics, S Chand & Company, New Delhi, tenth edition.
  3. Mood Alexander M., Graybill Frankline and Boes Duane C.(2007): Introduction to Theory of Statistics, Mc Graw Hill & Company Third Edition.

 

REFERENCES: 

SUGGESTED READINGS:

  1. Rohatgi, V.K.(2009): An Introduction to Probability Theory and Statistics, John Wiley And Sons.
  2. Casella, G. and Berger, Roger L.(2002): Statistical Inference, Duxbury Thompson Learning, Second Edition.
  3. Snedecor, G.W. and Cochran, W.G. (1967): Statistical Methods, Iowa State University Press.
  4. Rao, C. R. (2002): Linear Statistical Inference and its Applications, Willey- Blackwell
  5. Kiefer JC. (1987):  Introduction to Statistical Inference. Springer.

 

E-RESOURCES:

• https://epgp.inflibnet.ac.in/
• https://www.academia.edu/
• https://www.slideshare.net/

JOURNALS:

 

Academic Year: 
2022-23
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