Introduction
The basic aim of this course is to highlight the advance applications of probabilistic approaches in the concern of statistical paradigm. Course explores the importance of risk factors and effective decision making strategies. This course also classified according to their metric requirements (i.e., metric level, commensurability across dimensions, and lexicographic ordering) in the system, are given. A brief introduction to process tracing techniques is followed by a review of results reported in process tracing studies of decision making.
Learning Outcomes
At the completion of course, the student will be able to
Recommenced Books
Week
|
Topics and Readings
|
1 - 2
|
Definition of Bio-statistics, vis-à-vis the type of variables and observations in biological sciences
|
3 - 4
|
Health and medical sciences, Uniqueness in terms of behavior of variables their domain, and units
|
5 - 6
|
Categorical. Numerical and censored data
|
7 - 8
|
Population, Target populations and samples; Role of sampling in bio-statistics, Size of samples of various types of studies
|
9 - 10
|
Proportions, rates and rations; incidence, prevalence and odds. Distributional behavior of biological variables (Binomial, Poisson and Normal)
|
11- 12
|
Role of transformation for analysis of biological variables
|
13 - 14
|
Probit and Logit transformations and their analysis, p values, its importance and role
|
15 - 16
|
Confidence interval in simple and composite hypothesis testing
|