Statistical Inference and Sampling (Theory)

Paper Code: 
24CBDA316
Credits: 
03
Periods/week: 
03
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: 

Course

Learning Outcome

 (at course level)

Learning and       teaching strategies

Assessment Strategies

Course Code

Course Title

24CBDA 316

Statistical Inference and Sampling

(Theory)

 

CO157. Analyse properties of good estimators and apply fundamental principles of statistical inference.

CO158. Perform point estimation and interval estimation under a variety of discrete and continuous probability models.

CO159. Apply the applications of sampling distributions to the real-world problems.

CO160. Construct hypothesis test about population mean and proportion.

CO161. Analyse and conduct sample surveys by using an appropriate Sampling Technique.

CO162.Contribute effectively in course-specific interaction

Approach in teaching:

Interactive Lectures, Group Discussion, Case Study

 

Learning activities for the students:

Self-learning assignments, Machine Learning exercises, presentations

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

 

Unit I: 
Point estimation and Interval Estimation:

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

Unit II: 
Maximum Likelihood and its properties :

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.

 

Unit III: 
Hypothesis Testing:

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

 

Unit IV: 
Central limit theorem and Sampling:

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.

 

Unit V: 
Concept of Sampling Design:

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: 

SUGGESTED TEXT BOOKS

  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 REFERENCE BOOKS

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:

       1. https://epgp.inflibnet.ac.in/
       2. https://www.academia.edu/
       3.  https://www.slideshare.net/

       JOURNALS:

       https://www.sciencegate.app/keyword/445436

Academic Year: 
2024-25
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