###### Mathematics  Applied Multivariate Statistical Modeling-2
 List Lectures   [ 1 ]  2  3
 # Lecture Name 1 Lecture 1: Introduction to multivariate statistical modeling' Part'I 2 Lecture'2: Introduction to multivariate statistical modeling' Part'II 3 Lecture_3: Univariate descriptive statistics 4 Lecture_4: Sampling Distribution 5 Lec'05 Estimation' Part'I 6 Lec'06: Estimation'Part'II 7 Lec'07: Hypothesis Testing 8 Lecture'8: Introduction to multivariate statistical modeling' Part'I 9 Lecture'9: Introduction to multivariate statistical modeling' Part'II 10 Lecture 10 : Multivariate Normal Distribution

 Title: Applied Multivariate Statistical Modeling-2 Department: Mathematics Author: Dr J Maiti University: IIT Kharagpur Type: WebLink Abstract: Data driven decision making is the state of the art today. Engineers today gather huge data and seek meaningful knowledge out of these for interpreting the process behaviour. Scientists do experiments under controlled environment and analyse them to confirm or reject hypotheses. Managers and administrators use the results out of data analysis for day to day decision making. As the data collected and stored are multidimensional, to extract knowledge out of it requires statistical analysis in the multivariate domain. The aim of this course is therefore to build confidence in the students in analysing and interpreting multivariate data. The course will help the students by: (i) Providing guidelines to identify and describe real life problems so that relevant data can be collected, (ii) Linking data generation process with statistical distributions, especially in the multivariate domain, (iii) Linking the relationship among the variables (of a process or system) with multivariate statistical models, (iv) Providing step by step procedure for estimating parameters of a model developed, (v) Analysing errors along with computing overall fit of the models, (vi) Interpreting model results in real life problem solving, and (vii) Providing procedures for model validation.

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