| Module Name | Download |
|---|---|
| noc21_mg77_assignment_Week_0 | noc21_mg77_assignment_Week_0 |
| noc21_mg77_assignment_Week_1 | noc21_mg77_assignment_Week_1 |
| noc21_mg77_assignment_Week_10 | noc21_mg77_assignment_Week_10 |
| noc21_mg77_assignment_Week_11 | noc21_mg77_assignment_Week_11 |
| noc21_mg77_assignment_Week_2 | noc21_mg77_assignment_Week_2 |
| noc21_mg77_assignment_Week_3 | noc21_mg77_assignment_Week_3 |
| noc21_mg77_assignment_Week_4 | noc21_mg77_assignment_Week_4 |
| noc21_mg77_assignment_Week_5 | noc21_mg77_assignment_Week_5 |
| noc21_mg77_assignment_Week_6 | noc21_mg77_assignment_Week_6 |
| noc21_mg77_assignment_Week_7 | noc21_mg77_assignment_Week_7 |
| noc21_mg77_assignment_Week_8 | noc21_mg77_assignment_Week_8 |
| noc21_mg77_assignment_Week_9 | noc21_mg77_assignment_Week_9 |
| noc21_mg77assignment_Week12 | noc21_mg77assignment_Week12 |
| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Lecture 01: Overview of Module 01 & Introduction of Causality | Download |
| 2 | Lecture 02: Correlation and Causality | Download |
| 3 | Lecture 03: Correlation and Causality (Contd.) | Download |
| 4 | Lecture 04: Correlation and Causality (Contd.) | Download |
| 5 | Lecture 05: Probability Theory | Download |
| 6 | Lecture 06: Probability Theory (Contd.) | Download |
| 7 | Lecture 07: Probability Theory (Contd.) | Download |
| 8 | Lecture 08: Probability Theory (Contd.) | Download |
| 9 | Lecture 09: Posterior Probability | Download |
| 10 | Lecture 10: Bayesian Theorem | Download |
| 11 | Lecture 11: Bayesian Theorem (Contd.): Repeated Trial | Download |
| 12 | Lecture 12: Bayesian Theorem (Contd.): Example of Diamond Identification | Download |
| 13 | Lecture 13: Probability Distribution | Download |
| 14 | Lecture 14: Double Structure of Variable | Download |
| 15 | Lecture 15: Probability Distribution (Discrete/Continuous Variable) Random Variable | Download |
| 16 | Lecture 16: Probability Mass Function (PMF) Probability Density Function (PDF)" | Download |
| 17 | Lecture 17: Expectation, Variance, Covariance | Download |
| 18 | Lecture 18: Expectation, Variance, Covariance (Contd.) | Download |
| 19 | Lecture 19: Covariance Rule | Download |
| 20 | Lecture 20: Bernoulli Distribution | Download |
| 21 | Lecture 21: Bernoulli Distribution (Contd.) | Download |
| 22 | Lecture 22: Normal Approximation of Bernoulli Distribution | Download |
| 23 | Lecture 23: Sampling | Download |
| 24 | Lecture 24: Sampling (Contd.) | Download |
| 25 | Lecture 25: Central Limit Theorem | Download |
| 26 | Lecture 26: Law of Large Numbers LLN | Download |
| 27 | Lecture 27: Properties of Estimator | Download |
| 28 | Lecture 28: Conflict Between Unbiasedness and Min Variance | Download |
| 29 | Lecture 29: T - Distribution | Download |
| 30 | Lecture 30: Normal Distribution | Download |
| 31 | Lecture 31: Normal Distribution (Contd.) | Download |
| 32 | Lecture 32: Hypothesis Testing | Download |
| 33 | Lecture 33: Decision Rules | Download |
| 34 | Lecture 34: Level of Significance | Download |
| 35 | Lecture 35: P Value | Download |
| 36 | Lecture 36: Power of a Test | Download |
| 37 | Lecture 37: Confidence Interval | Download |
| 38 | Lecture 38: Confidence Interval Example | Download |
| 39 | Lecture 39: Properties of Power of a Test | Download |
| 40 | Lecture 40: Introduction to Module II | Download |
| 41 | Lecture 41: Error Term, Coefficient of Determination, Regression Coefficient | Download |
| 42 | Lecture 42: Error Term, Coefficient of Determination, Regression Coefficient (Contd.) | Download |
| 43 | Lecture 43: Error Term, Coefficient of Determination, Regression Coefficient (Contd.) | Download |
| 44 | Lecture 44: Definition : Variable, Parameter and Coefficient | Download |
| 45 | Lecture 45: Introduction to Regression: Recapitulating Correlation and Causal Thinking | Download |
| 46 | Lecture 46: Adjusted R-Squared | Download |
| 47 | Lecture 47: Degrees of Freedom | Download |
| 48 | Lecture 48: Multiple Regression | Download |
| 49 | Lecture 49: Multiple Regression (Contd.) | Download |
| 50 | Lecture 50: Regression Table | Download |
| 51 | Lecture 51: Regression Table (Contd.) | Download |
| 52 | Lecture 52: Multicollinearity | Download |
| 53 | Lecture 53: Multicollinearity (Contd.) | Download |
| 54 | Lecture 54: Multicollinearity (Contd.) | Download |
| 55 | Lecture 55: Multicollinearity (Contd.) | Download |
| 56 | Lecture 56: Multicollinearity (Contd.) | Download |
| 57 | Lecture 57: Dummy Variable | Download |
| 58 | Lecture 58: Dummy variable (Contd.) | Download |
| 59 | Lecture 59: Dummy variable (Contd.) | Download |
| 60 | Lecture 60: Dummy variable (Contd.) | Download |
| 61 | Lecture 61: Dummy variable (Contd.) | Download |
| 62 | Lecture 62: Dummy variable (Contd.) | Download |
| 63 | Lecture 63: Dummy variable (Contd.) | Download |
| 64 | Lecture 64: Heteroscedasticity | Download |
| 65 | Lecture 65: Heteroscedasticity (Contd.) | Download |
| 66 | Lecture 66 : Heteroscedasticity (Contd.) | Download |
| 67 | Lecture 67 : Heteroscedasticity (Contd.) | Download |
| 68 | Lecture 68 : Heteroscedasticity (Contd.) | Download |
| 69 | Lecture 69 : Heteroscedasticity (Contd.) | Download |
| 70 | Lecture 70: Autocorrelation | Download |
| 71 | Lecture 71: Autocorrelation (Contd.) | Download |
| 72 | Lecture 72: Autocorrelation (Contd.) | Download |
| 73 | Lecture 73: Autocorrelation (Contd.) | Download |
| 74 | Lecture 74: Autocorrelation (Contd.) | Download |
| 75 | Lecture 75: Autocorrelation (Contd.) | Download |
| 76 | Lecture 76: Autocorrelation (Contd.) | Download |
| 77 | Lecture 77: Autocorrelation (Contd.) | Download |
| 78 | Lecture 78: Autocorrelation (Contd.) | Download |
| 79 | Lecture 79: Autocorrelation (Contd.) | Download |
| 80 | Lecture 80: Autocorrelation (Contd.) | Download |
| 81 | Lecture 81: Autocorrelation (Contd.) | Download |
| 82 | Lecture 82: Remedy for Autocorrelation | Download |
| 83 | Lecture 83: Model Specification | Download |
| 84 | Lecture 84: Model Specification (Contd.) | Download |
| 85 | Lecture 85: Model Specification (Contd.) | Download |
| 86 | Lecture 86: Model Specification (Contd.) | Download |
| 87 | Lecture 87: Model Specification (Contd.) | Download |
| 88 | Lecture 88: Model Specification (Contd.) | Download |
| 89 | Lecture 89: Model Specification (Contd.) | Download |
| 90 | Lecture 90: Model Specification (Contd.) | Download |
| 91 | Lecture 91 : Continuation with Proxy Variable | Download |
| 92 | Lecture 92: Ramsey Reset Test | Download |
| 93 | Lecture 93 : Introduction to Module III | Download |
| 94 | Lecture 94 : Non Stochastic Regressor | Download |
| 95 | Lecture 95 : Stochastic Regressor | Download |
| 96 | Lecture 96 : Assumptions for Regression Models with Non-Stochastic Regressor | Download |
| 97 | Lecture 97 : Assumptions for Regression Model with Stochastic Regressor | Download |
| 98 | Lecture 98 : Instrumental Variable | Download |
| 99 | Lecture 99 : Instrumental Variable (Contd.) | Download |
| 100 | Lecture 100: Asymptotic Property | Download |
| 101 | Lecture 101: Problem of Endogeneity | Download |
| 102 | Lecture 102: Simultaneous Equation Model | Download |
| 103 | Lecture 103: Instrumental Variable for Endogeneity Bias Problem | Download |
| 104 | Lecture 104: Good Bad and Weak Instrumental Variable | Download |
| 105 | Lecture 105 : Overidentification Underidentification Exact Identification - Instrumental Variable | Download |
| 106 | Lecture 106 : Two Stage Least Square and Instrumental Variable | Download |
| 107 | Lecture 107 : 2SLS and IV with Stata | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Lecture 01: Overview of Module 01 & Introduction of Causality | Download Verified |
| 2 | Lecture 02: Correlation and Causality | PDF unavailable |
| 3 | Lecture 03: Correlation and Causality (Contd.) | Download Verified |
| 4 | Lecture 04: Correlation and Causality (Contd.) | Download Verified |
| 5 | Lecture 05: Probability Theory | Download Verified |
| 6 | Lecture 06: Probability Theory (Contd.) | Download Verified |
| 7 | Lecture 07: Probability Theory (Contd.) | Download Verified |
| 8 | Lecture 08: Probability Theory (Contd.) | Download Verified |
| 9 | Lecture 09: Posterior Probability | Download Verified |
| 10 | Lecture 10: Bayesian Theorem | Download Verified |
| 11 | Lecture 11: Bayesian Theorem (Contd.): Repeated Trial | Download Verified |
| 12 | Lecture 12: Bayesian Theorem (Contd.): Example of Diamond Identification | Download Verified |
| 13 | Lecture 13: Probability Distribution | Download Verified |
| 14 | Lecture 14: Double Structure of Variable | Download Verified |
| 15 | Lecture 15: Probability Distribution (Discrete/Continuous Variable) Random Variable | Download Verified |
| 16 | Lecture 16: Probability Mass Function (PMF) Probability Density Function (PDF)" | Download Verified |
| 17 | Lecture 17: Expectation, Variance, Covariance | Download Verified |
| 18 | Lecture 18: Expectation, Variance, Covariance (Contd.) | Download Verified |
| 19 | Lecture 19: Covariance Rule | Download Verified |
| 20 | Lecture 20: Bernoulli Distribution | Download Verified |
| 21 | Lecture 21: Bernoulli Distribution (Contd.) | PDF unavailable |
| 22 | Lecture 22: Normal Approximation of Bernoulli Distribution | PDF unavailable |
| 23 | Lecture 23: Sampling | PDF unavailable |
| 24 | Lecture 24: Sampling (Contd.) | PDF unavailable |
| 25 | Lecture 25: Central Limit Theorem | PDF unavailable |
| 26 | Lecture 26: Law of Large Numbers LLN | PDF unavailable |
| 27 | Lecture 27: Properties of Estimator | PDF unavailable |
| 28 | Lecture 28: Conflict Between Unbiasedness and Min Variance | PDF unavailable |
| 29 | Lecture 29: T - Distribution | PDF unavailable |
| 30 | Lecture 30: Normal Distribution | PDF unavailable |
| 31 | Lecture 31: Normal Distribution (Contd.) | PDF unavailable |
| 32 | Lecture 32: Hypothesis Testing | PDF unavailable |
| 33 | Lecture 33: Decision Rules | PDF unavailable |
| 34 | Lecture 34: Level of Significance | PDF unavailable |
| 35 | Lecture 35: P Value | PDF unavailable |
| 36 | Lecture 36: Power of a Test | PDF unavailable |
| 37 | Lecture 37: Confidence Interval | PDF unavailable |
| 38 | Lecture 38: Confidence Interval Example | PDF unavailable |
| 39 | Lecture 39: Properties of Power of a Test | PDF unavailable |
| 40 | Lecture 40: Introduction to Module II | PDF unavailable |
| 41 | Lecture 41: Error Term, Coefficient of Determination, Regression Coefficient | PDF unavailable |
| 42 | Lecture 42: Error Term, Coefficient of Determination, Regression Coefficient (Contd.) | PDF unavailable |
| 43 | Lecture 43: Error Term, Coefficient of Determination, Regression Coefficient (Contd.) | PDF unavailable |
| 44 | Lecture 44: Definition : Variable, Parameter and Coefficient | PDF unavailable |
| 45 | Lecture 45: Introduction to Regression: Recapitulating Correlation and Causal Thinking | PDF unavailable |
| 46 | Lecture 46: Adjusted R-Squared | PDF unavailable |
| 47 | Lecture 47: Degrees of Freedom | PDF unavailable |
| 48 | Lecture 48: Multiple Regression | PDF unavailable |
| 49 | Lecture 49: Multiple Regression (Contd.) | PDF unavailable |
| 50 | Lecture 50: Regression Table | PDF unavailable |
| 51 | Lecture 51: Regression Table (Contd.) | PDF unavailable |
| 52 | Lecture 52: Multicollinearity | PDF unavailable |
| 53 | Lecture 53: Multicollinearity (Contd.) | PDF unavailable |
| 54 | Lecture 54: Multicollinearity (Contd.) | PDF unavailable |
| 55 | Lecture 55: Multicollinearity (Contd.) | PDF unavailable |
| 56 | Lecture 56: Multicollinearity (Contd.) | PDF unavailable |
| 57 | Lecture 57: Dummy Variable | PDF unavailable |
| 58 | Lecture 58: Dummy variable (Contd.) | PDF unavailable |
| 59 | Lecture 59: Dummy variable (Contd.) | PDF unavailable |
| 60 | Lecture 60: Dummy variable (Contd.) | PDF unavailable |
| 61 | Lecture 61: Dummy variable (Contd.) | PDF unavailable |
| 62 | Lecture 62: Dummy variable (Contd.) | PDF unavailable |
| 63 | Lecture 63: Dummy variable (Contd.) | PDF unavailable |
| 64 | Lecture 64: Heteroscedasticity | PDF unavailable |
| 65 | Lecture 65: Heteroscedasticity (Contd.) | PDF unavailable |
| 66 | Lecture 66 : Heteroscedasticity (Contd.) | PDF unavailable |
| 67 | Lecture 67 : Heteroscedasticity (Contd.) | PDF unavailable |
| 68 | Lecture 68 : Heteroscedasticity (Contd.) | PDF unavailable |
| 69 | Lecture 69 : Heteroscedasticity (Contd.) | PDF unavailable |
| 70 | Lecture 70: Autocorrelation | PDF unavailable |
| 71 | Lecture 71: Autocorrelation (Contd.) | PDF unavailable |
| 72 | Lecture 72: Autocorrelation (Contd.) | PDF unavailable |
| 73 | Lecture 73: Autocorrelation (Contd.) | PDF unavailable |
| 74 | Lecture 74: Autocorrelation (Contd.) | PDF unavailable |
| 75 | Lecture 75: Autocorrelation (Contd.) | PDF unavailable |
| 76 | Lecture 76: Autocorrelation (Contd.) | PDF unavailable |
| 77 | Lecture 77: Autocorrelation (Contd.) | PDF unavailable |
| 78 | Lecture 78: Autocorrelation (Contd.) | PDF unavailable |
| 79 | Lecture 79: Autocorrelation (Contd.) | PDF unavailable |
| 80 | Lecture 80: Autocorrelation (Contd.) | PDF unavailable |
| 81 | Lecture 81: Autocorrelation (Contd.) | PDF unavailable |
| 82 | Lecture 82: Remedy for Autocorrelation | PDF unavailable |
| 83 | Lecture 83: Model Specification | PDF unavailable |
| 84 | Lecture 84: Model Specification (Contd.) | PDF unavailable |
| 85 | Lecture 85: Model Specification (Contd.) | PDF unavailable |
| 86 | Lecture 86: Model Specification (Contd.) | PDF unavailable |
| 87 | Lecture 87: Model Specification (Contd.) | PDF unavailable |
| 88 | Lecture 88: Model Specification (Contd.) | PDF unavailable |
| 89 | Lecture 89: Model Specification (Contd.) | PDF unavailable |
| 90 | Lecture 90: Model Specification (Contd.) | PDF unavailable |
| 91 | Lecture 91 : Continuation with Proxy Variable | PDF unavailable |
| 92 | Lecture 92: Ramsey Reset Test | PDF unavailable |
| 93 | Lecture 93 : Introduction to Module III | PDF unavailable |
| 94 | Lecture 94 : Non Stochastic Regressor | PDF unavailable |
| 95 | Lecture 95 : Stochastic Regressor | PDF unavailable |
| 96 | Lecture 96 : Assumptions for Regression Models with Non-Stochastic Regressor | PDF unavailable |
| 97 | Lecture 97 : Assumptions for Regression Model with Stochastic Regressor | PDF unavailable |
| 98 | Lecture 98 : Instrumental Variable | PDF unavailable |
| 99 | Lecture 99 : Instrumental Variable (Contd.) | PDF unavailable |
| 100 | Lecture 100: Asymptotic Property | PDF unavailable |
| 101 | Lecture 101: Problem of Endogeneity | PDF unavailable |
| 102 | Lecture 102: Simultaneous Equation Model | PDF unavailable |
| 103 | Lecture 103: Instrumental Variable for Endogeneity Bias Problem | PDF unavailable |
| 104 | Lecture 104: Good Bad and Weak Instrumental Variable | PDF unavailable |
| 105 | Lecture 105 : Overidentification Underidentification Exact Identification - Instrumental Variable | PDF unavailable |
| 106 | Lecture 106 : Two Stage Least Square and Instrumental Variable | PDF unavailable |
| 107 | Lecture 107 : 2SLS and IV with Stata | PDF unavailable |
| Sl.No | Language | Book link |
|---|---|---|
| 1 | English | Not Available |
| 2 | Bengali | Not Available |
| 3 | Gujarati | Not Available |
| 4 | Hindi | Not Available |
| 5 | Kannada | Not Available |
| 6 | Malayalam | Not Available |
| 7 | Marathi | Not Available |
| 8 | Tamil | Not Available |
| 9 | Telugu | Not Available |