Overview

COURSE OUTLINE: Through this course, students can obtain exposure on data analysis, modeling and spreadsheet use with BUSINESS ANALYTICS for DECISION MAKING. This course will be exclusively quantitative and an application to business/management-related problems. It is connected with problem sets and real-life cases to know the relevance of a particular problem and the decision-making thereof.

Syllabus

Engineering Econometrics by Prof. Rudra P Pradhan.
Lecture 1 : Introduction to Engineering Econometrics.
Lecture 2 : Introduction to Engineering Econometrics (Contd.).
Lecture 3 : Introduction to Engineering Econometrics (Contd.).
Lecture 4 : Introduction to Engineering Econometrics (Contd.).
Lecture 5 : Introduction to Engineering Econometrics (Contd.).
Lecture 6 : Exploring Data on Spreadsheets.
Lecture 7 : Exploring Data on Spreadsheets (Contd.).
Lecture 8 : Exploring Data on Spreadsheets (Contd.).
Lecture 9 : Exploring Data on Spreadsheets (Contd.).
Lecture 10 : Exploring Data on Spreadsheets (Contd.).
Lecture 11 : Descriptive Econometrics.
Lecture 12 : Descriptive Econometrics (Contd.).
Lecture 13 : Descriptive Econometrics (Contd.).
Lecture 14 : Descriptive Econometrics (Contd.).
Lecture 15 : Descriptive Econometrics (Contd.).
Lecture 16 : Linear Regression Modelling.
Lecture 17 : Linear Regression Modelling (Contd.).
Lecture 18 : Linear Regression Modelling (Contd.).
Lecture 19 : Linear Regression Modelling (Contd.).
Lecture 20 : Linear Regression Modelling (Contd.).
Lecture 21 : Linear Regression Modelling (Contd.).
Lecture 22 : Linear Regression Modelling (Contd.).
Lecture 23 : Modelling Diagnostics.
Lecture 24 : Modelling Diagnostics (Contd.).
Lecture 25 : Modelling Diagnostics (Contd.).
Lecture 26 : Multicolinearity problem (Contd.).
Lecture 27 : Autocorrelation problem.
Lecture 28 : Autocorrelation problem (Contd.).
Lecture 29 : Heteroskedasticity problem.
Lecture 30 : Heteroskedasticity problem (Contd.).
Lecture 31 : Model Specification- Choosing the Independent Variables.
Lecture 32 : Model Specification- Choosing the Independent Variables (Contd.).
Lecture 33 : Non-Linear Regression Modelling- Dummy-Variable Regression Modelling.
Lecture 34 : Non-Linear Regression Modelling- Interactive Regression Modelling.
Lecture 35 :Non-Linear Regression Modelling- Polynomial (Curvilinear) Regression Model.
Lecture 36 : Non-Linear Regression Modelling _Model Transformation.
Lecture 37 : Extension of Dummy Regression Modelling.
Lecture 38 : Extension of Dummy Regression Modelling- Dummy Independent Variable Modelling.
Lecture 39 : Extension of Dummy Regression Modelling- Dummy Dependent Variable Modelling.
Lecture 40 : Extension of Dummy Regression Modelling- Dummy Independent Variable Modelling.
Lecture 41 : Time Series Modelling- Basics.
Lecture 42 : Time Series Modelling- Trend Analysis.
Lecture 43 : Time Series Modelling- Trend Analysis (Least Squares Method).
Lecture 44 : Time Series Modelling- Forecasting.
Lecture 45 : Time Series Modelling- Stationarity.
Lecture 46 : Time Series Modelling- Volatility Modelling.
Lecture 47 : Time Series Modelling- Volatility Modelling (Contd.).
Lecture 48 : Time Series Modelling- Volatility Modelling (Contd.).
Lecture 49 : Time Series Modelling- Volatility Modelling (Contd.).
Lecture 50 : Time Series Modelling- Volatility Modelling (Contd.).
Lecture 51 : Time Series Modelling- VAR modelling.
Lecture 52: Time Series Modelling- VAR modelling y.
Lecture 53: Panel Data Modelling.
Lecture 54 : Panel Data Modelling (Contd.).
Lecture 55 : Panel Data Modelling (Contd.).
Lecture 56 : Panel Data Modelling.
Lecture 57 : Fitting Models to Data.
Lecture 58 : Fitting Models to Data (Contd.).
Lecture 59 : Fitting Models to Data (Contd.).
Lecture 60 : Fitting Models to Data (Contd.).