MST 011 Real Analysis, Calculus and Geometry | Quick Readable Notes For IGNOU Exam 
MST 012 Probability and Probability Distributions | Quick Readable Notes For IGNOU Exam 
MST 013 Survey Sampling and Design of Experiments-I | Quick Readable Notes For IGNOU Exam 
MST 014 Statistical Quality Control and Time Series Analysis | Quick Readable Notes For IGNOU Exam 
MST 015 Introduction to R Software | Quick Readable Notes For IGNOU Exam 
MST 017 Applied Regression Analysis | Quick Readable Notes For IGNOU Exam 
MST 018 Multivariate Analysis | Quick Readable Notes For IGNOU Exam 
MST 019 Epidemiology and Clinical Trials | Quick Readable Notes For IGNOU Exam 
MST 020 Survey Sampling and Design of Experiments-II | Quick Readable Notes For IGNOU Exam 
MST 021 Classical and Bayesian Inference | Quick Readable Notes For IGNOU Exam 
MST 022 Linear Algebra and Multivariate Calculus | Quick Readable Notes For IGNOU Exam 
MST 023 Research Methodology | Quick Readable Notes For IGNOU Exam 
MST 024 Data Analysis with Python | Quick Readable Notes For IGNOU Exam 
MST 025 Categorical and Survival Analysis| Quick Readable Notes For IGNOU Exam 
MST 026 Introduction to Machine Learning | Quick Readable Notes For IGNOU Exam 
MSTE 011 Operations Research | Quick Readable Notes For IGNOU Exam 
MSTE 012 Stochastic Processes | Quick Readable Notes For IGNOU Exam MST 016 Statistical Inference | Quick Readable...
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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 |
| Author | : BMAP EDUSERVICES PVT LTD |
| Publisher | : BMAP EDUSERVICES PVT LTD |
| University | : IGNOU (Indira Gandhi National Open University) |
| Pages | : 100-130 |
| Weight | : 157gms |
| Dimensions | : 21.0 x 29.7 cm (A4 Size Pages) |
MST 016 Statistical Inference focuses on methods for making conclusions and predictions based on data analysis in applied statistics. Topics include hypothesis testing, confidence intervals, regression analysis, parametric and non-parametric methods, analysis of variance (ANOVA), and Bayesian inference.
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