MST 011 Real Analysis, Calculus and Geometry | Guess Papers of IGNOU 
MST 012 Probability and Probability Distributions | Guess Papers of IGNOU 
MST 013 Survey Sampling and Design of Experiments-I | Guess Papers of IGNOU 
MST 014 Statistical Quality Control and Time Series Analysis | Guess Papers of IGNOU 
MST 015 Introduction to R Software | Guess Papers of IGNOU 
MST 016 Statistical Inference | Guess Papers of IGNOU 
MST 017 Applied Regression Analysis | Guess Papers of IGNOU 
MST 018 Multivariate Analysis | Guess Papers of IGNOU 
MST 020 Survey Sampling and Design of Experiments-II | Guess Papers of IGNOU 
MST 021 Classical and Bayesian Inference | Guess Papers of IGNOU 
MST 021 Classical and Bayesian Inference | Guess Papers of IGNOU 
MST 022 Linear Algebra and Multivariate Calculus | Guess Papers of IGNOU 
MST 023 Research Methodology | Guess Papers of IGNOU 
MST 024 Data Analysis with Python | Guess Papers of IGNOU 
MST 025 Categorical and Survival Analysis | Guess Papers of IGNOU 
MST 026 Introduction to Machine Learning | Guess Papers of IGNOU 
MSTE 011 Operations Research | Guess Papers of IGNOU 
MSTE 012 Stochastic Processes | Guess Papers of IGNOU 
MST 019 Epidemiology and Clinical Trials | Quick...
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| Category | : MASTER‘S DEGREE PROGRAMMES |
| Sub Category | : Master of Science (Applied Statistics) (MSCAST) |
| Products Code | : MSCAST-MST-S2-5.51 |
| HSN Code | : 490110 |
| Language | : English, English |
| Author | : BMAP EDUSERVICES PVT LTD |
| Publisher | : BMAP EDUSERVICES PVT LTD |
| University | : IGNOU (Indira Gandhi National Open University) |
| Pages | : 50 |
| Weight | : 157gms |
| Dimensions | : 21.0 x 29.7 cm (A4 Size Pages) |
MST 019 Epidemiology and Clinical Trials offers an extensive understanding of epidemiological concepts and clinical trial methodologies. Students will learn about the design, implementation, and analysis of clinical trials, which are crucial for advancing medical research and public health initiatives.
MST 018 Multivariate Analysis delves into the statistical methods used to analyze data involving multiple variables. This course covers techniques such as factor analysis, cluster analysis, and principal component analysis, providing students with the skills needed to interpret complex data sets.
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