99年第1學期-1400 抽樣調查 課程資訊
|quiz 1||20||Do not miss the exam.|
|midterm||30||Do not miss the exam.|
|quiz 2||20||Do not miss the exam.|
|final||30||Do not miss the exam.|
The aims of this course are:
(1) Learn the setups and the mechanics of sampling procedures,
(2) Learn the computations associated with sampling,
(3) Make meaningful inferences concerning the population based on (1) and (2).
Sampling is widely used in the modern world. The statistical offices of many nations have sample surveys conducted on topics of interest such as unemployment, size of labor force etc.. Furthermore, sample surveys are often conducted by company on topics of the behavior of consumers.
Sampling design determines the precision of the estimates. Thus, the way a sample is drawn is as important as the mathematical form of the estimator. Sample design consists of both a sample selection plan and an estimation procedure. In this course, we shall ﬁrst deﬁne population, sampling units (primary, secondary, etc.); then introduce many sampling schemes, such as simple random sampling (with or without replacement), stratiﬁed sampling (optimum allocation of sampling units to various strata), multistage sampling (e.g. two-stage stratiﬁed cluster sampling), systematic sampling, double sampling, and sampling with unequal probabilities (with or without replacement). We shall also introduce ratio estimator, regression estimator, and poststratiﬁcation estimators.
Finally, we shall derive the variances of the proposed estimators and the estimators of their variances. For estimating the variance of the estimators, the topics of Jackknife method and bootstrap method are also included in this course.