Shalabh
shalab@iitk.ac.in
shalabh1@yahoo.com
Department of Mathematics & Statistics
Indian Institute of Technology Kanpur, Kanpur - 208016 (India)

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MTH 432M/432A : Introduction to Sampling Theory

Syllabus : Principles of sample surveys; Simple, stratified and unequal probability sampling with and without replacement; ratio, product and regression method of estimation, Varying Probability Scheme

Books: You can choose any one of the following book for your reference. Books at serial numbers 1 and 2 are easily available, so I will base my lectures on them. Other books are available in the library.

1.      Sampling Techniques : W.G. Cochran, Wiley (Low price edition available)

2.      Theory and Methods of Survey Sampling : Parimal Mukhopadhyay, Prentice Hall of India

3.      Theory of Sample surveys with applications : P.V. Sukhatme, B.V Sukhatme, S. Sukhatme and C. Asok, IASRI, Delhi

4.      Sampling Methodologies and Applications : P.S.R.S. Rao, Chapman and Hall/ CRC

5.      Sampling Theory and Methods : M.N. Murthy, Statistical Publishing Society, Calcutta (Out of print)

6.      Elements of sampling theory and methods : Z. Govindrajalu, Prentice Hall

7.   Sampling Methods- Exercises and Solutions : Pascal Ardilly and Yves Tille' (Download here through IITK Library link)

 

Course Policy: Earn your marks and grades. I will be the happiest instructor to award the best grades to all the students.

Grading Scheme: Quiz- 40%,   Mid Sem.- 60%

Class Schedule: Class begins: 31 July 2023. Time table: Mon, Wed, Thu, Fri 8:00 Hrs - 8:50 Hrs. 

Mid Semester Examination:  

Contact hours: 24 X 7, by email, phone, what's app. (If possible and not so urgent, avoid calling between 12-7 AM.)

Announcements:

Assignments: The solutions of all the assignments are to be submitted and they will be graded.

Assignment 1

Assignment 2

Assignment 3

Assignment 4

Assignment 5

Assignment 6

A link will be sent through email to all the students to upload their assignments.

 

Lecture notes for your help (If you find any typo, please let me know)

For the course MTH432 A, Lecture Notes 2, 3, 4, 5, 6 and 7  are required.

Other chapters are detailed for knowledge enhancement.

Lecture Notes 1 : Introduction

Lecture Notes 2 : Simple Random Sampling

Lecture Notes 3 : Sampling For Proportions and Percentages

Lecture Notes 4 : Stratified Sampling

Lecture Notes 5 : Ratio and Product Methods of Estimation

Lecture Notes 6  : Regression Method of Estimation

Lecture Notes 7  : Varying Probability Sampling

Lecture Notes 8  : Double Sampling (Two Phase Sampling)

Lecture Notes 9  : Cluster Sampling

Lecture Notes 10  : Two Stage Sampling (Subsampling)

Lecture Notes 11  :  Systematic Sampling

Lecture Notes 12  : Sampling on Successive Occasions

Lecture Notes 13  : Non Sampling Errors

 

Lecture Videos:

The students are advised to access the video lectures via the MooKIT platform as the lectures may have pop up quiz. Missing the pop up quiz will be considered as absent or zero marks.

Introductory Video (at Youtube)

 

Slides and videos used in the lectures

(Lectures 1,2,3 are for those who have not studied Sampling Theory in UG Classes.

Lecture 20 and 23 were not possible to cover in the usual classroom teaching, so it is not a part of the syllabus in online mode but it is given for completeness)

The video lectures are also available at Swayam Prabha DTH Channel 16 Youtube link: The telecasted lectures are available at  YouTube (Click here)

 

 

Lecture videos download links

Lecture Slides download links

Brief Description

Lecture Title

Lecture No.

Click here Lecture 1

Click here Lecture 1

Basic definitions

Basic Definitions and Fundamentals

1*

Click here Lecture 2

Click here Lecture 2

Ensuring representativeness, Advantages of Sampling Over Complete Enumeration, Type of Surveys

Basic Definitions and Fundamentals

2*

Click here Lecture 3

Click here Lecture 3

Principal Steps in Conducting a Survey, Methods of Data Collection

Basic Definitions and Fundamentals

3*

Click here Lecture 4

Click here Lecture 4

SRSWOR, SRSWR and basic concepts

Simple Random Sampling

4

Click here Lecture 5

Click here Lecture 5

Probability of Selection of a Sample and a Unit

Simple Random Sampling

5

Click here Lecture 6

Click here Lecture 6

Estimation of population mean and variance

Simple Random Sampling

6

Click here Lecture 7

Click here Lecture 7

Estimation of variance and Confidence interval estimation  

Simple Random Sampling

7

Click here Lecture 8

Click here Lecture 8

Confidence interval estimation  and Sample size determination

Simple Random Sampling

8

Click here Lecture 9

Click here Lecture 9

SRSWOR, SRSWR and basic concepts

Simple Random Sampling for Proportions and Percentages

9

Click here Lecture 10

Click here Lecture 10

Estimation of population mean and related topics

Simple Random Sampling for Proportions and Percentages

10

Click here Lecture 11

Click here Lecture 11

Basic concepts and sampling procedure

Stratified Sampling

11

Click here Lecture 12

Click here Lecture 12

Advantages of  Stratified Sampling and Estimation of Population Mean and Variance

Stratified Sampling

12

Click here Lecture 13

Click here Lecture 13

Sample allocation and the Variances of stratum mean under allocations

Stratified Sampling

13

Click here Lecture 14

Click here Lecture 14

Allocation, variance under allocations, proportions

Stratified Sampling

14

Click here Lecture 15

Click here Lecture 15

Basic concepts and Bias of Ratio Estimator

Ratio Method of Estimation

15

Click here Lecture 16

Click here Lecture 16

Mean Squared Error of Ratio Estimator

Ratio Method of Estimation

16

Click here Lecture 17

Click here Lecture 17

Efficiency of Ratio Estimator, Upper Limit of Ratio Estimator and estimate of MSE

Ratio Method of Estimation

17

Click here Lecture 18

Click here Lecture 18

Ratio Estimator in Stratified Sampling

Ratio Method of Estimation

18

Click here Lecture 19

Click here Lecture 19

Unbiased Ratio-type Estimators and Product method of estimation

Unbiased Ratio Type Estimators and Product Method of Estimation

19

Click here Lecture 20

Click here Lecture 20

Post Stratification and MSE of Multivariate Ratio Estimator

Post Stratification and Multivariate Ratio Estimator

20*

Click here Lecture 21

Click here Lecture 21

Basics and fundamentals, Regression estimates with pre-assigned regression coefficient, estimate of variance

Regression Method of Estimation

21

Click here Lecture 22

Click here Lecture 22

Bias and Mean Squared Error of the Regression Estimates, Comparison with Sample Random Sampling

Regression Method of Estimation

22

Click here Lecture 23

Click here Lecture 23

Regression method in stratified sampling

Regression Method of Estimation

23*

Click here Lecture 24

Click here Lecture 24

Basic definitions and concepts

Varying Probability Sampling

24

Click here Lecture 25

Click here Lecture 25

Probability proportional to size and sample drawing methods

Varying Probability Sampling

25

Click here Lecture 26

Click here Lecture 26

PPS in sampling with replacement and related topics

Varying Probability Sampling

26

Click here Lecture 27

Click here Lecture 27

PPS in sampling without  replacement and ordered estimators

Varying Probability Sampling

27

Click here Lecture 28

Click here Lecture 28

Unordered estimators, Murthy's  Estimator and Horwitz Thompson Estimator and related topics

Varying Probability Sampling

28

Click here Lecture 29

Click here Lecture 29

Horwitz Thompson Estimator and Midzuno System of Sampling

Varying Probability Sampling

29

 

Following videos are only for knowledge enhancement and not the part of syllabus.

 

Click here Lecture 30

Click here Lecture 30

Double sampling in ratio method of estimation

Double Sampling

30

Click here Lecture 31

Click here Lecture 31

Double sampling in regression method of estimation

Double Sampling

31

Click here Lecture 32

Click here Lecture 32

Basic concepts, Estimation of population mean and Variance with equal size clusters

Cluster Sampling

32

Click here Lecture 33

Click here Lecture 33

Comparison of cluster sampling with simple random sampling

Cluster Sampling

33

Click here Lecture 34

Click here Lecture 34

Estimation of a Proportion in case of Equal Cluster, estimation of population mean with unequal size clusters

Cluster Sampling

34

Click here Lecture 35

Click here Lecture 35

Basic concepts and estimation of population mean with unequal size clusters

Cluster Sampling

35

Click here Lecture 36

Click here Lecture 36

Basic concepts and estimation of population mean with equal first stage units

Two Stage Sampling

36

Click here Lecture 37

Click here Lecture 37

Basic concepts and estimation of population mean with unequal first stage units

Two Stage Sampling

37

Click here Lecture 38

Click here Lecture 38

Basic fundamentals, definitions and estimation of population mean

Systematic Sampling

38

Click here Lecture 39

Click here Lecture 39

Various results of systematic sampling and its relation to other sampling schemes

Systematic Sampling

39

Click here Lecture 40

Click here Lecture 40

Basic fundamentals and definitions

Non Sampling Errors

40