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Course Detail

Course Name Biostatistics
Course Code 24AIM311
Program B.Tech. in Artificial Intelligence (AI) and Data Science (Medical Engineering)
Semester VI
Credits 3
Campus Coimbatore

Syllabus

Unit 1

Need of biostatistics Descriptive statistics: Population and samples descriptive methods for categorical data descriptive methods for continuous data probability and probability distributions types of data frequency distribution measures of central tendency measures of variability kurtosis and skewness Z score

Unit 2

Inferential statistics Parameters estimating and comparing the mean of population. Hypothesis testing: basic concepts and steps testing normal distribution – Kolmogorov-Simon test testing homogeneity of variance – Levine’s test Z-tests dependent t-test, independent t-test, t-test as GLM, F-test, Chi-square test Type I and type II errors ANOVA, ANCOVA, factorial ANOVA, repeated-measures designs, mixed design ANOVA, post hoc procedures.

Unit 3

Non-parametric tests non-parametric and distribution-free tests – Mann-Whitney test Wilcoxon signed-rank test, Wilcoxon signed rank sum test, Kruskal-Wallis test, Friedman’s ANOVA.

Unit 4

Correlation techniques Bivariate correlation – Pearson’s correlation coefficient, Spearman’s correlation coefficient Partial correlation regression – method of least squares, assessing goodness of fit multiple regression. Experimental design and clinical trials.

Course Objectives and Outcomes

Course Objectives:

To impart training on basic biostatistics and use of various statistical tools for biomedical data analysis.
To apply statistical tools in experimental design and clinical trials

Course Outcomes:

After completing this course, students should be able to
CO1: Effectively analyze and summarize both categorical and continuous data using descriptive statistics.
CO2: Apply inferential statistics, including hypothesis testing and parametric tests, for meaningful interpretations and decisions.
CO3: Demonstrate proficiency in non-parametric tests and correlation techniques for data analysis.
CO4: Utilize statistical analysis and understand its applications in experimental design and clinical trials.

CO-PO Mapping

PO/P

SO

PO 1  

PO2

 

PO3

 

PO4

 

PO5

PO 6  

PO7

 

PO8

PO 9 PO 10 PO 11 PO 12 PSO 1  

PSO2

 

PSO3

CO
CO1 1 2 2 2 3 2 2
CO2 2 3 2 2 2 3 2 2
CO3 3 1 3 2 2 2 3 2
CO4 1 1 2 2 2 2 2 3 2 2

References

  1. Eberly, L. E., Le, C. T., Introductory Biostatistics. Germany: Wiley, 2016.
  2. Glaser, A. N. High-yield Biostatistics. United States: Lippincott Williams & Wilkins, 2001.
  3. Advances in Clinical Trial Biostatistics, United States: Taylor & Francis. 2003.

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