STATS 332 (Survey Planning and Contingency
Tables)
Instructor: Sarjinder Singh
Office: 141(ECC)
Lecture Room: T, H (11:00-12:15) ECC 118
Office hours: (M, T, W, H, F
1:00pm—3:00pm) or by appointment
E-mails: [email protected] or
[email protected]
Phone: (320) –654 5324
Stats 332 introduces the basic sampling techniques and mathematical models to show how they enhance critical thinking and reasoning. This course gives the concepts of collection, description, and making of generalizations from data and its importance in our daily lives.
Book(s): Sampling: Design and Analysis.
Sharon L. Lohr, Duxbury Press.
Schedule
|
Lect. No. |
Day |
Date |
Contents |
Sections |
|
|
|
|
September |
|
|
1 |
H |
04 |
Definition of Statistics, Sample, Requirements of a
good sample, Selection Bias, Measurement bias, Questionnaire Design. |
1.2,1.3, 1.4,
1.5 |
|
2 |
T |
09 |
Sampling and Non sampling errors, Probability
sampling, bias, variance and mean square error, standard deviation and
coefficient of variation |
1.6, 2.2 |
|
3 |
H |
11 |
Simple random sampling: with replacement (WR) and
without replacement (WOR), estimator of mean and its variance under WR and
WOR sampling, proportion as a special case of mean. |
2.3 |
|
4 |
T |
16 |
Need of confidence Intervals estimates,
Sample size determination, Ratio estimation, Bias and mse expressions,
comparison with sample mean. Idea of product estimation.
|
2.4, 2.5, 3.1, 3.1.2, 3.1.2.2 |
|
5 |
H |
18 |
Numerical exercises related to ratio estimator |
|
|
6 |
T |
23 |
Difference and regression estimator |
3.2 |
|
7 |
H |
25 |
Estimation in domain, Models for ratio and
regression estimation, comparison between model-based and design based
estimates. |
3.3, 3.4 |
|
8 |
T
|
30 |
Exercises related to regression and difference
estimator |
3.2 |
|
|
|
|
October
|
|
|
9 |
H |
02 |
Stratified sampling, estimation of mean or total
in stratified sampling, Equal allocation, Proportional allocation and Optimum
allocation. |
4.1, 4.2, 4.4 |
|
10 |
T |
07 |
Stratified sampling for proportions, numerical
exercises related to stratified sampling. |
4.2 |
|
11 |
H
|
09 |
Post
stratification, Exercise related to post stratification |
4.7 |
|
12 |
T
|
14 |
Exam – I
|
|
|
|
|
|
Cluster sampling, Notation for cluster sampling,
Estimation of mean using clusters of equal size
|
5.1, 5.1.1 |
|
13 |
H |
16 |
Exercises related to equal size clusters, Idea of
unequal clusters, estimation of ratio |
5.2.3 |
|
14 |
T |
21 |
Two-stage cluster sampling, estimation of ratio, Using weights in cluster sampling, |
5.3, 5.4 |
|
15 |
H |
23 |
Idea of systematic sampling, Exercises related to systematic sampling |
5.6 |
|
16 |
T |
28 |
Probability proportional to size and with replacement
sampling, cumulative total method,
Lahiri’s method |
6.2, 6.2.1, 6.2.2 |
|
17 |
H |
30 |
Estimation of total using unequal selection probabilities, Exercises related to unequal probability sampling |
6.2.3 |
|
|
|
|
November
|
|
|
18 |
T |
04 |
Unequal probability sampling
without replacement sampling, Midzuno–Sen sampling scheme and a numerical exercise, Horvitz and Thompson
estimator, its variance, two estimators of variance |
6.4, 6.4.1 |
|
19 |
H |
06 |
More exercises related to
HT-estimator
|
|
|
20 |
T |
11 |
Idea of two-phase sampling, handling of
non-response viz. Call backs,
weighting method and imputation. |
8.3 |
|
21 |
H |
13 |
Exercises handing non-response, BRR in stratified sampling
|
9.3.1 |
|
22 |
T |
18 |
Idea of Jackknife with ratio estimator example |
9.3.2 |
|
23 |
H |
20 |
Exam – II |
|
|
24 |
T |
25 |
Idea of hypothesis, Chi-square test with
multinomial sampling |
10.1 |
|
25 |
H |
27 |
Thanks giving holidays
|
|
|
|
|
|
December |
|
|
26 |
T |
02 |
Testing
independence of factors, Testing homogeneity of proportions, Test of goodness
of fit, Effect of survey design on chi-square test |
10.1.1, 10.1.2, 10.1.3, 10.2 |
|
27 |
H |
04 |
Contingency table for data from complex surveys, effect on hypothesis tests and confidence intervals, Correction to chi-square test, Wald and Bonferroni tests |
10.2.1, 10.2.2, 10.3 10.4.1 |
|
28 |
T |
09 |
Loglinear models with multinomial sampling,
Model-based regression in SRS |
|
|
29 |
H
|
11 |
Idea of logistic regression, Generalized
regression estimation for population total, Last day (Discussion and
questions)
|
11.1, 11.6 |
Note: It is tentative schedule. The
material from one lecture to another may be shifted if required. Some material
may be added or dropped.
|
HW# |
Due date |
Assignments |
|
1 |
Sept 23 |
|
|
2 |
Oct 14 |
Assignments will be given in the
class
|
|
3 |
Nov 04 |
|
|
4 |
Nov 21 |
|
|
5 |
Dec 09 |
|
|
Assignments |
5 times 4% each |
20% |
|
Term exams |
2 times 15% each |
30% |
|
Final Exam |
1 times 50% |
50% |
|
Final Grade |
|
100% |
Grades
|
90-100% |
85-89% |
80-84% |
75-79% |
70-74% |
65-69% |
60-64% |
55-59% |
50-54% |
45-49% |
0-45% |
|
A |
A- |
B+ |
B |
B- |
C+ |
C |
C- |
D+ |
D |
F |
Remarks: 1. All examinations will be ‘closed book’. Calculators are allowed, but no formulae sheets.
2. 20% marks will be deducted from late assignments.
3. Date and time for the final exam will be announced in the class. Experience shows that the date(s) for the midterm exams generally changes, so be regular with the class activities/announcements.
4. Assignments will be given in the class. A few students feel that assignments dead lines should be a bit flexible, and other feel not. Both kind of experience will be tried.
5. Any other change in the schedule will be announced in the class.
6. Please make sure that you are registered with this section, as otherwise your final grade may not be submitted.