Lecture 1: Introduction to Statistics

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Lecture 2: Introduction to Econometrics

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Lecture 3: Organization & Presentation of Data

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Lecture 4: Summarizing Data through Descriptive Statistics

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Lecture 5: Discrete Random Variable & Probability Distribution

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Lecture 6: Continuous Random Variables & Probability Distribution

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Lecture 7: Normal Distribution

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Lecture 8: Introduction to Statistical Inference

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Lecture 9: Estimation (Part I)

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Lecture 10: Estimation (Part II)

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Lecture 11: Hypothesis Testing (Part I)

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Lecture 12: Hypothesis Testing (Part II)

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Lecture 13: Hypothesis Testing (Part III)

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Lecture 14: Hypothesis Testing (Part IV)

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Lecture 15: Relationship between Qualitative Variables

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Lecture 16: Relationship Between Quantitative Variables

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Lecture 17: Analysis of Variance

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Lecture 18: One Way ANOVA

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Lecture 19: Two Way ANOVA

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Lecture 20: Analysis of Covariance

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Lecture 21: Index Number (Part I)

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Lecture 22: Index Numbers (Part II)

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Lecture 23: Classical Time Series Analysis (Part I)

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Lecture 24: Classical Time Series Analysis (Part II)

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Lecture 25: Classical Linear Regression Model (Part-I)

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Lecture 26: Classical Linear Regression Model (Part-II)

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Lecture 28: Hypothesis Testing with CNLRM

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Lecture 29: More on Hypothesis Testing and Model Specification

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Lecture 30: Violations of CLRM Assumptions (Heteroskedasticity)

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

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Lecture 32: Time Series Regression with Stationary Data

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Lecture 33: Time Series Regression with Non-Stationary Data

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Lecture 34: Regression with Dummy Explanatory Variable

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Lecture 35: Dummy Dependent Variable Models (Part-I)

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Lecture 36: Dummy Dependent Variable Models (Part-II)

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Lecture 37: Simultaneous Equations Model

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Lecture 38: Panel Data Regression

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Lecture 39: Program Evaluation

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Lecture 40: Data Analysis and Regression with R

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Lecture 41: Regression Involving Dummy Variables in R

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Lecture 42: Time Series Analysis in R

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