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

  • Understand and explicit the variables relating to biological sciences.
  • Colect, present, analyse the data relating to biological and health sciences and interpretation of results.
  • Carry on research work in above mentioned fields.

Recommenced Books

1. Zar, J. (2000) “ Biostatistical Analysis” 5th Edition, John Wiley and Sons.
2. Shoukri, M.M & Pause, C.C. (1998). “Statistical Methods for Health Sciences" 2nd Edition, CRC press, Florida
 
Evaluation Criteria
 
Sessional: 20
Assignment: 10
Presentation: 10
Mid Term Test: 30 
Final Exam: 50
Time Table Reg: Wednesday - Thursday 11:00am - 12:30pm
 
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
 

Course Material