Modules / Lectures
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Sl.No Chapter Name MP4 Download
1Lecture 01: Overview of Module 01 & Introduction of CausalityDownload
2Lecture 02: Correlation and CausalityDownload
3Lecture 03: Correlation and Causality (Contd.)Download
4Lecture 04: Correlation and Causality (Contd.)Download
5Lecture 05: Probability TheoryDownload
6Lecture 06: Probability Theory (Contd.)Download
7Lecture 07: Probability Theory (Contd.)Download
8Lecture 08: Probability Theory (Contd.)Download
9Lecture 09: Posterior ProbabilityDownload
10Lecture 10: Bayesian TheoremDownload
11Lecture 11: Bayesian Theorem (Contd.): Repeated TrialDownload
12Lecture 12: Bayesian Theorem (Contd.): Example of Diamond IdentificationDownload
13Lecture 13: Probability DistributionDownload
14Lecture 14: Double Structure of VariableDownload
15Lecture 15: Probability Distribution (Discrete/Continuous Variable) Random VariableDownload
16Lecture 16: Probability Mass Function (PMF) Probability Density Function (PDF)"Download
17Lecture 17: Expectation, Variance, CovarianceDownload
18Lecture 18: Expectation, Variance, Covariance (Contd.)Download
19Lecture 19: Covariance RuleDownload
20Lecture 20: Bernoulli DistributionDownload
21Lecture 21: Bernoulli Distribution (Contd.)Download
22Lecture 22: Normal Approximation of Bernoulli DistributionDownload
23Lecture 23: SamplingDownload
24Lecture 24: Sampling (Contd.)Download
25Lecture 25: Central Limit TheoremDownload
26Lecture 26: Law of Large Numbers LLNDownload
27Lecture 27: Properties of EstimatorDownload
28Lecture 28: Conflict Between Unbiasedness and Min VarianceDownload
29Lecture 29: T - DistributionDownload
30Lecture 30: Normal DistributionDownload
31Lecture 31: Normal Distribution (Contd.)Download
32Lecture 32: Hypothesis TestingDownload
33Lecture 33: Decision RulesDownload
34Lecture 34: Level of SignificanceDownload
35Lecture 35: P ValueDownload
36Lecture 36: Power of a TestDownload
37Lecture 37: Confidence IntervalDownload
38Lecture 38: Confidence Interval ExampleDownload
39Lecture 39: Properties of Power of a TestDownload
40Lecture 40: Introduction to Module IIDownload
41Lecture 41: Error Term, Coefficient of Determination, Regression CoefficientDownload
42Lecture 42: Error Term, Coefficient of Determination, Regression Coefficient (Contd.)Download
43Lecture 43: Error Term, Coefficient of Determination, Regression Coefficient (Contd.)Download
44Lecture 44: Definition : Variable, Parameter and CoefficientDownload
45Lecture 45: Introduction to Regression: Recapitulating Correlation and Causal ThinkingDownload
46Lecture 46: Adjusted R-SquaredDownload
47Lecture 47: Degrees of FreedomDownload
48Lecture 48: Multiple RegressionDownload
49Lecture 49: Multiple Regression (Contd.)Download
50Lecture 50: Regression TableDownload
51Lecture 51: Regression Table (Contd.)Download
52Lecture 52: MulticollinearityDownload
53Lecture 53: Multicollinearity (Contd.)Download
54Lecture 54: Multicollinearity (Contd.)Download
55Lecture 55: Multicollinearity (Contd.)Download
56Lecture 56: Multicollinearity (Contd.)Download
57Lecture 57: Dummy VariableDownload
58Lecture 58: Dummy variable (Contd.)Download
59Lecture 59: Dummy variable (Contd.)Download
60Lecture 60: Dummy variable (Contd.)Download
61Lecture 61: Dummy variable (Contd.)Download
62Lecture 62: Dummy variable (Contd.)Download
63Lecture 63: Dummy variable (Contd.)Download
64Lecture 64: HeteroscedasticityDownload
65Lecture 65: Heteroscedasticity (Contd.)Download
66Lecture 66 : Heteroscedasticity (Contd.)Download
67Lecture 67 : Heteroscedasticity (Contd.)Download
68Lecture 68 : Heteroscedasticity (Contd.)Download
69Lecture 69 : Heteroscedasticity (Contd.)Download
70Lecture 70: AutocorrelationDownload
71Lecture 71: Autocorrelation (Contd.)Download
72Lecture 72: Autocorrelation (Contd.)Download
73Lecture 73: Autocorrelation (Contd.)Download
74Lecture 74: Autocorrelation (Contd.)Download
75Lecture 75: Autocorrelation (Contd.)Download
76Lecture 76: Autocorrelation (Contd.)Download
77Lecture 77: Autocorrelation (Contd.)Download
78Lecture 78: Autocorrelation (Contd.)Download
79Lecture 79: Autocorrelation (Contd.)Download
80Lecture 80: Autocorrelation (Contd.)Download
81Lecture 81: Autocorrelation (Contd.)Download
82Lecture 82: Remedy for AutocorrelationDownload
83Lecture 83: Model SpecificationDownload
84Lecture 84: Model Specification (Contd.)Download
85Lecture 85: Model Specification (Contd.)Download
86Lecture 86: Model Specification (Contd.)Download
87Lecture 87: Model Specification (Contd.)Download
88Lecture 88: Model Specification (Contd.)Download
89Lecture 89: Model Specification (Contd.)Download
90Lecture 90: Model Specification (Contd.)Download
91Lecture 91 : Continuation with Proxy VariableDownload
92Lecture 92: Ramsey Reset TestDownload
93Lecture 93 : Introduction to Module IIIDownload
94Lecture 94 : Non Stochastic RegressorDownload
95Lecture 95 : Stochastic RegressorDownload
96Lecture 96 : Assumptions for Regression Models with Non-Stochastic RegressorDownload
97Lecture 97 : Assumptions for Regression Model with Stochastic RegressorDownload
98Lecture 98 : Instrumental VariableDownload
99Lecture 99 : Instrumental Variable (Contd.)Download
100Lecture 100: Asymptotic PropertyDownload
101Lecture 101: Problem of EndogeneityDownload
102Lecture 102: Simultaneous Equation ModelDownload
103Lecture 103: Instrumental Variable for Endogeneity Bias ProblemDownload
104Lecture 104: Good Bad and Weak Instrumental VariableDownload
105Lecture 105 : Overidentification Underidentification Exact Identification - Instrumental VariableDownload
106Lecture 106 : Two Stage Least Square and Instrumental VariableDownload
107Lecture 107 : 2SLS and IV with StataDownload

Sl.No Chapter Name English
1Lecture 01: Overview of Module 01 & Introduction of CausalityDownload
Verified
2Lecture 02: Correlation and CausalityPDF unavailable
3Lecture 03: Correlation and Causality (Contd.)Download
Verified
4Lecture 04: Correlation and Causality (Contd.)Download
Verified
5Lecture 05: Probability TheoryDownload
Verified
6Lecture 06: Probability Theory (Contd.)Download
Verified
7Lecture 07: Probability Theory (Contd.)Download
Verified
8Lecture 08: Probability Theory (Contd.)Download
Verified
9Lecture 09: Posterior ProbabilityDownload
Verified
10Lecture 10: Bayesian TheoremDownload
Verified
11Lecture 11: Bayesian Theorem (Contd.): Repeated TrialDownload
Verified
12Lecture 12: Bayesian Theorem (Contd.): Example of Diamond IdentificationDownload
Verified
13Lecture 13: Probability DistributionDownload
Verified
14Lecture 14: Double Structure of VariableDownload
Verified
15Lecture 15: Probability Distribution (Discrete/Continuous Variable) Random VariableDownload
Verified
16Lecture 16: Probability Mass Function (PMF) Probability Density Function (PDF)"Download
Verified
17Lecture 17: Expectation, Variance, CovarianceDownload
Verified
18Lecture 18: Expectation, Variance, Covariance (Contd.)Download
Verified
19Lecture 19: Covariance RuleDownload
Verified
20Lecture 20: Bernoulli DistributionDownload
Verified
21Lecture 21: Bernoulli Distribution (Contd.)PDF unavailable
22Lecture 22: Normal Approximation of Bernoulli DistributionPDF unavailable
23Lecture 23: SamplingPDF unavailable
24Lecture 24: Sampling (Contd.)PDF unavailable
25Lecture 25: Central Limit TheoremPDF unavailable
26Lecture 26: Law of Large Numbers LLNPDF unavailable
27Lecture 27: Properties of EstimatorPDF unavailable
28Lecture 28: Conflict Between Unbiasedness and Min VariancePDF unavailable
29Lecture 29: T - DistributionPDF unavailable
30Lecture 30: Normal DistributionPDF unavailable
31Lecture 31: Normal Distribution (Contd.)PDF unavailable
32Lecture 32: Hypothesis TestingPDF unavailable
33Lecture 33: Decision RulesPDF unavailable
34Lecture 34: Level of SignificancePDF unavailable
35Lecture 35: P ValuePDF unavailable
36Lecture 36: Power of a TestPDF unavailable
37Lecture 37: Confidence IntervalPDF unavailable
38Lecture 38: Confidence Interval ExamplePDF unavailable
39Lecture 39: Properties of Power of a TestPDF unavailable
40Lecture 40: Introduction to Module IIPDF unavailable
41Lecture 41: Error Term, Coefficient of Determination, Regression CoefficientPDF unavailable
42Lecture 42: Error Term, Coefficient of Determination, Regression Coefficient (Contd.)PDF unavailable
43Lecture 43: Error Term, Coefficient of Determination, Regression Coefficient (Contd.)PDF unavailable
44Lecture 44: Definition : Variable, Parameter and CoefficientPDF unavailable
45Lecture 45: Introduction to Regression: Recapitulating Correlation and Causal ThinkingPDF unavailable
46Lecture 46: Adjusted R-SquaredPDF unavailable
47Lecture 47: Degrees of FreedomPDF unavailable
48Lecture 48: Multiple RegressionPDF unavailable
49Lecture 49: Multiple Regression (Contd.)PDF unavailable
50Lecture 50: Regression TablePDF unavailable
51Lecture 51: Regression Table (Contd.)PDF unavailable
52Lecture 52: MulticollinearityPDF unavailable
53Lecture 53: Multicollinearity (Contd.)PDF unavailable
54Lecture 54: Multicollinearity (Contd.)PDF unavailable
55Lecture 55: Multicollinearity (Contd.)PDF unavailable
56Lecture 56: Multicollinearity (Contd.)PDF unavailable
57Lecture 57: Dummy VariablePDF unavailable
58Lecture 58: Dummy variable (Contd.)PDF unavailable
59Lecture 59: Dummy variable (Contd.)PDF unavailable
60Lecture 60: Dummy variable (Contd.)PDF unavailable
61Lecture 61: Dummy variable (Contd.)PDF unavailable
62Lecture 62: Dummy variable (Contd.)PDF unavailable
63Lecture 63: Dummy variable (Contd.)PDF unavailable
64Lecture 64: HeteroscedasticityPDF unavailable
65Lecture 65: Heteroscedasticity (Contd.)PDF unavailable
66Lecture 66 : Heteroscedasticity (Contd.)PDF unavailable
67Lecture 67 : Heteroscedasticity (Contd.)PDF unavailable
68Lecture 68 : Heteroscedasticity (Contd.)PDF unavailable
69Lecture 69 : Heteroscedasticity (Contd.)PDF unavailable
70Lecture 70: AutocorrelationPDF unavailable
71Lecture 71: Autocorrelation (Contd.)PDF unavailable
72Lecture 72: Autocorrelation (Contd.)PDF unavailable
73Lecture 73: Autocorrelation (Contd.)PDF unavailable
74Lecture 74: Autocorrelation (Contd.)PDF unavailable
75Lecture 75: Autocorrelation (Contd.)PDF unavailable
76Lecture 76: Autocorrelation (Contd.)PDF unavailable
77Lecture 77: Autocorrelation (Contd.)PDF unavailable
78Lecture 78: Autocorrelation (Contd.)PDF unavailable
79Lecture 79: Autocorrelation (Contd.)PDF unavailable
80Lecture 80: Autocorrelation (Contd.)PDF unavailable
81Lecture 81: Autocorrelation (Contd.)PDF unavailable
82Lecture 82: Remedy for AutocorrelationPDF unavailable
83Lecture 83: Model SpecificationPDF unavailable
84Lecture 84: Model Specification (Contd.)PDF unavailable
85Lecture 85: Model Specification (Contd.)PDF unavailable
86Lecture 86: Model Specification (Contd.)PDF unavailable
87Lecture 87: Model Specification (Contd.)PDF unavailable
88Lecture 88: Model Specification (Contd.)PDF unavailable
89Lecture 89: Model Specification (Contd.)PDF unavailable
90Lecture 90: Model Specification (Contd.)PDF unavailable
91Lecture 91 : Continuation with Proxy VariablePDF unavailable
92Lecture 92: Ramsey Reset TestPDF unavailable
93Lecture 93 : Introduction to Module IIIPDF unavailable
94Lecture 94 : Non Stochastic RegressorPDF unavailable
95Lecture 95 : Stochastic RegressorPDF unavailable
96Lecture 96 : Assumptions for Regression Models with Non-Stochastic RegressorPDF unavailable
97Lecture 97 : Assumptions for Regression Model with Stochastic RegressorPDF unavailable
98Lecture 98 : Instrumental VariablePDF unavailable
99Lecture 99 : Instrumental Variable (Contd.)PDF unavailable
100Lecture 100: Asymptotic PropertyPDF unavailable
101Lecture 101: Problem of EndogeneityPDF unavailable
102Lecture 102: Simultaneous Equation ModelPDF unavailable
103Lecture 103: Instrumental Variable for Endogeneity Bias ProblemPDF unavailable
104Lecture 104: Good Bad and Weak Instrumental VariablePDF unavailable
105Lecture 105 : Overidentification Underidentification Exact Identification - Instrumental VariablePDF unavailable
106Lecture 106 : Two Stage Least Square and Instrumental VariablePDF unavailable
107Lecture 107 : 2SLS and IV with StataPDF unavailable


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