NPTEL : NOC:Applied Statistics and Econometrics (Humanities and Social Sciences)

Co-ordinators : Prof. Deep Mukherjee


Lecture 1 - Introduction to Statistics

Lecture 2 - Introduction to Econometrics

Lecture 3 - Organization and Presentation of Data

Lecture 4 - Summarizing Data through Descriptive Statistics

Lecture 5 - Discrete Random Variable and Probability Distribution

Lecture 6 - Continuous Random Variables and Probability Distribution

Lecture 7 - Normal Distribution

Lecture 8 - Introduction to Statistical Inference

Lecture 9 - Estimation - Part I

Lecture 10 - Estimation - Part II

Lecture 11 - Hypothesis Testing - Part I

Lecture 12 - Hypothesis Testing - Part II

Lecture 13 - Hypothesis Testing - Part III

Lecture 14 - Hypothesis Testing - Part IV

Lecture 15 - Relationship between Qualitative Variables

Lecture 16 - Relationship Between Quantitative Variables

Lecture 17 - Analysis of Variance

Lecture 18 - One Way ANOVA

Lecture 19 - Two Way ANOVA

Lecture 20 - Analysis of Covariance

Lecture 21 - Index Numbers - Part I

Lecture 22 - Index Numbers - Part II

Lecture 23 - Classical Time Series Analysis - Part I

Lecture 24 - Classical Time Series Analysis - Part II

Lecture 25 - Classical Linear Regression Model - Part I

Lecture 26 - Classical Linear Regression Model - Part II

Lecture 27 - Classical Linear Regression Model

Lecture 28 - Hypothesis Testing with CNLRM

Lecture 29 - More on Hypothesis Testing and Model Specification

Lecture 30 - Violations of CLRM Assumptions (Heteroskedasticity)

Lecture 31 - Violations of CLRM Assumptions (Autocorrelation and Multicollinearity)

Lecture 32 - Time Series Regression with Stationary Data

Lecture 33 - Time Series Regression with Non-Stationary Data

Lecture 34 - Regression with Dummy Explanatory Variable

Lecture 35 - Dummy Dependent Variable Models - Part I

Lecture 36 - Dummy Dependent Variable Models - Part II

Lecture 37 - Simultaneous Equations Model

Lecture 38 - Panel Data Regression

Lecture 39 - Program Evaluation

Lecture 40 - Data Analysis and Regression with R

Lecture 41 - Regression Involving Dummy Variables in R

Lecture 42 - Time Series Analysis in R