Lecture 01: Overview of Module 01 & Introduction of Causality

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Lecture 02: Correlation and Causality

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Lecture 03: Correlation and Causality (Contd.)

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Lecture 04: Correlation and Causality (Contd.)

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Lecture 05: Probability Theory

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Lecture 06: Probability Theory (Contd.)

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Lecture 07: Probability Theory (Contd.)

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Lecture 08: Probability Theory (Contd.)

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Lecture 09: Posterior Probability

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Lecture 10: Bayesian Theorem

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Lecture 11: Bayesian Theorem (Contd.): Repeated Trial

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Lecture 12: Bayesian Theorem (Contd.): Example of Diamond Identification

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Lecture 13: Probability Distribution

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Lecture 14: Double Structure of Variable

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Lecture 15: Probability Distribution (Discrete/Continuous Variable) Random Variable

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Lecture 16: Probability Mass Function (PMF) Probability Density Function (PDF)"

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Lecture 17: Expectation, Variance, Covariance

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Lecture 18: Expectation, Variance, Covariance (Contd.)

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Lecture 19: Covariance Rule

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Lecture 20: Bernoulli Distribution

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Lecture 21: Bernoulli Distribution (Contd.)

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Lecture 22: Normal Approximation of Bernoulli Distribution

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Lecture 23: Sampling

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Lecture 24: Sampling (Contd.)

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Lecture 25: Central Limit Theorem

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Lecture 26: Law of Large Numbers LLN

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Lecture 27: Properties of Estimator

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Lecture 28: Conflict Between Unbiasedness and Min Variance

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Lecture 29: T - Distribution

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Lecture 30: Normal Distribution

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Lecture 31: Normal Distribution (Contd.)

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Lecture 32: Hypothesis Testing

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Lecture 33: Decision Rules

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Lecture 34: Level of Significance

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ecture 35: P Value

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Lecture 36: Power of a Test

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Lecture 37: Confidence Interval

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Lecture 38: Confidence Interval Example

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Lecture 39: Properties of Power of a Test

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Lecture 40: Introduction to Module II

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Lecture 41: Error Term, Coefficient of Determination, Regression Coefficient

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Lecture 42: Error Term, Coefficient of Determination, Regression Coefficient (Contd.)

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Lecture 43: Error Term, Coefficient of Determination, Regression Coefficient (Contd.)

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Lecture 44: Definition : Variable, Parameter and Coefficient

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Lecture 45: Introduction to Regression: Recapitulating Correlation and Causal Thinking

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Lecture 46: Adjusted R-Squared

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Lecture 47: Degrees of Freedom

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Lecture 48: Multiple Regression

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Lecture 49: Multiple Regression (Contd.)

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Lecture 50: Regression Table

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Lecture 51: Regression Table (Contd.)

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Lecture 52: Multicollinearity

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Lecture 53: Multicollinearity (Contd.)

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Lecture 54: Multicollinearity (Contd.)

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Lecture 55: Multicollinearity (Contd.)

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Lecture 56: Multicollinearity (Contd.)

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Lecture 57: Dummy Variable

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Lecture 58: Dummy variable (Contd.)

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Lecture 59: Dummy variable (Contd.)

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Lecture 60: Dummy variable (Contd.)

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Lecture 61: Dummy variable (Contd.)

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Lecture 62: Dummy variable (Contd.)

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Lecture 63: Dummy variable (Contd.)

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Lecture 64: Heteroscedasticity

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Lecture 65: Heteroscedasticity (Contd.)

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Lecture 66 : Heteroscedasticity (Contd.)

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Lecture 67 : Heteroscedasticity (Contd.)

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Lecture 68 : Heteroscedasticity (Contd.)

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Lecture 69 : Heteroscedasticity (Contd.)

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Lecture 70: Autocorrelation

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Lecture 71: Autocorrelation (Contd.)

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Lecture 72: Autocorrelation (Contd.)

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Lecture 73: Autocorrelation (Contd.)

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Lecture 74: Autocorrelation (Contd.)

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Lecture 75: Autocorrelation (Contd.)

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Lecture 76: Autocorrelation (Contd.)

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Lecture 77: Autocorrelation (Contd.)

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Lecture 78: Autocorrelation (Contd.)

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Lecture 79: Autocorrelation (Contd.)

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Lecture 80: Autocorrelation (Contd.)

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Lecture 81: Autocorrelation (Contd.)

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Lecture 82: Remedy for Autocorrelation

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Lecture 83: Model Specification

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Lecture 84: Model Specification (Contd.)

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Lecture 85: Model Specification (Contd.)

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Lecture 86: Model Specification (Contd.)

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Lecture 87: Model Specification (Contd.)

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Lecture 88: Model Specification (Contd.)

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Lecture 89: Model Specification (Contd.)

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Lecture 90: Model Specification (Contd.)

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Lecture 91 : Continuation with Proxy Variable

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Lecture 92: Ramsey Reset Test

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Lecture 93 : Introduction to Module III

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Lecture 94 : Non Stochastic Regressor

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Lecture 95 : Stochastic Regressor

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Lecture 96 : Assumptions for Regression Models with Non-Stochastic Regressor

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Lecture 97 : Assumptions for Regression Model with Stochastic Regressor

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Lecture 98 : Instrumental Variable

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Lecture 99 : Instrumental Variable (Contd.)

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Lecture 100: Asymptotic Property

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Lecture 101: Problem of Endogeneity

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Lecture 102: Simultaneous Equation Model

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Lecture 103: Instrumental Variable for Endogeneity Bias Problem

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Lecture 104: Good Bad and Weak Instrumental Variable

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Lecture 105 : Overidentification Underidentification Exact Identification - Instrumental Variable

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Lecture 106 : Two Stage Least Square and Instrumental Variable

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Lecture 107 : 2SLS and IV with Stata

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