Канал: Econometrics and Analytics

Business analytics II - Week 7 - 04 Designing Conclusive Experiments

Business analytics II - Week 7 - 04 Designing Conclusive Experiments

Business analytics II - Week 7 - 03 Split Testing and A B Tests

Business analytics II - Week 7 - 03 Split Testing and A B Tests

Business analytics II - Week 7 - 02 Preparing for Experiments

Business analytics II - Week 7 - 02 Preparing for Experiments

Business analytics II - Week 7 - 01 Experiments and Causality Issues

Business analytics II - Week 7 - 01 Experiments and Causality Issues

Business analytics II - Week 6 - 06 Checking Components of Time series

Business analytics II - Week 6 - 06 Checking Components of Time series

Business analytics II - Week 6 - 05 Cycles

Business analytics II - Week 6 - 05 Cycles

Business analytics II - Week 6 - 04 Seasonality

Business analytics II - Week 6 - 04 Seasonality

Business analytics II - Week 6 - 02 Time Series Visualization and Smoothing

Business analytics II - Week 6 - 02 Time Series Visualization and Smoothing

Business analytics II - Week 6 - 01 Introduction to Time Series and Forecasting

Business analytics II - Week 6 - 01 Introduction to Time Series and Forecasting

Business analytics II - Week 6 - 03 Time Series Components and Trends

Business analytics II - Week 6 - 03 Time Series Components and Trends

Business analytics II - Week 5 - 06 Out of Sample Validation

Business analytics II - Week 5 - 06 Out of Sample Validation

Business analytics II - Week 5 - 05 About Best Subsets and Stepwise Regression

Business analytics II - Week 5 - 05 About Best Subsets and Stepwise Regression

Business analytics II - Week 5 - 04 Best subsets Regression (with R)

Business analytics II - Week 5 - 04 Best subsets Regression (with R)

Business analytics II - Week 5 - 03 Stepwise Regression (with R)

Business analytics II - Week 5 - 03 Stepwise Regression (with R)

Business analytics II - Week 5 - 02 Multicollinearity

Business analytics II - Week 5 - 02 Multicollinearity

Business analytics II - Week 5 - 01 Model Building Considerations

Business analytics II - Week 5 - 01 Model Building Considerations

Business Analytics II - Week 4 - 07 Non Linear Transformations

Business Analytics II - Week 4 - 07 Non Linear Transformations

Business Analytics II - Week 4 - 06 Linear Transformations

Business Analytics II - Week 4 - 06 Linear Transformations

Business Analytics II - Week 4 - 05 Interaction Terms

Business Analytics II - Week 4 - 05 Interaction Terms

Business Analytics II - Week 4 - 04 Categorical Variables

Business Analytics II - Week 4 - 04 Categorical Variables

Business Analytics II - Week 4 - 03 Model Comparison and Adjusted R2

Business Analytics II - Week 4 - 03 Model Comparison and Adjusted R2

Business Analytics II - Week 4 - 02 Fitted and Incremental Values in Multiple Regression

Business Analytics II - Week 4 - 02 Fitted and Incremental Values in Multiple Regression

Business Analytics II - Week 4 - 01 Introducing Multiple Regression

Business Analytics II - Week 4 - 01 Introducing Multiple Regression

Business Analytics II - Week 3 - 10 Regression: Assumptions

Business Analytics II - Week 3 - 10 Regression: Assumptions

Business Analytics II - Week 3 - 09 Regression: Residuals vs Population Errors

Business Analytics II - Week 3 - 09 Regression: Residuals vs Population Errors

Business Analytics II - Week 3 - 08 Regression: Practical Significance

Business Analytics II - Week 3 - 08 Regression: Practical Significance

Business Analytics II - Week 3 - 07 Regression: Confidence Intervals

Business Analytics II - Week 3 - 07 Regression: Confidence Intervals

Business Analytics II - Week 3 - 06 Regression: p-values

Business Analytics II - Week 3 - 06 Regression: p-values

Business Analytics II - Week 3 - 05 Regression: Interpreting Coefficients and Standard Errors

Business Analytics II - Week 3 - 05 Regression: Interpreting Coefficients and Standard Errors

Business Analytics II - Week 3 - 04 Regression: Causality

Business Analytics II - Week 3 - 04 Regression: Causality

Business Analytics II - Week 3 - 03 Regression: R Squared

Business Analytics II - Week 3 - 03 Regression: R Squared

Business Analytics II - Week 3 - 02 Regression: Line of Best Fit

Business Analytics II - Week 3 - 02 Regression: Line of Best Fit

Business Analytics II - Week 3 - 01 Scatterplots to Regression

Business Analytics II - Week 3 - 01 Scatterplots to Regression

Business Analytics II - Week 2 - 02 Correlation is Not Causation

Business Analytics II - Week 2 - 02 Correlation is Not Causation

Business Analytics II - Week 2 - 01 Scatterplots and correlations

Business Analytics II - Week 2 - 01 Scatterplots and correlations

Business Analytics II - Week 1 - 07 Hypothesis Testing

Business Analytics II - Week 1 - 07 Hypothesis Testing

Business Analytics II - Week 1 - 06 Sampling

Business Analytics II - Week 1 - 06 Sampling

Business Analytics II - Week 1 - 05 Normal distribution

Business Analytics II - Week 1 - 05 Normal distribution

Business Analytics II - Week 1 - 04 Probability Distributions

Business Analytics II - Week 1 - 04 Probability Distributions

Business Analytics II - Week 1 - 03 Histograms

Business Analytics II - Week 1 - 03 Histograms

Business Analytics II - Week 1 - 02 Terminology

Business Analytics II - Week 1 - 02 Terminology

Business Analytics II - Week 1 - 01 Introduction and Motivation

Business Analytics II - Week 1 - 01 Introduction and Motivation

ECO310 - Intro to R : 04 - Dummy variables, Interactions and Trends

ECO310 - Intro to R : 04 - Dummy variables, Interactions and Trends

ECO310 - Intro to R : 03 - Linear Regression

ECO310 - Intro to R : 03 - Linear Regression

ECO310 - Intro to R : 02 - Visualizing Data

ECO310 - Intro to R : 02 - Visualizing Data

ECO310 - Intro to R : 01 - Loading Data and Generating Variables

ECO310 - Intro to R : 01 - Loading Data and Generating Variables

ECO375 - Exercise 2.11

ECO375 - Exercise 2.11

ECO375 - Introduction to Stata

ECO375 - Introduction to Stata

ECO375F - 6.2 - Wald Estimator (Binary Instrumental Variable)

ECO375F - 6.2 - Wald Estimator (Binary Instrumental Variable)

ECO375F - 6.1 - Instrumental Variables: Definition and Derivation of the Estimator

ECO375F - 6.1 - Instrumental Variables: Definition and Derivation of the Estimator

ECO375F - 5.4 - Proof of consistency for the OLS estimator

ECO375F - 5.4 - Proof of consistency for the OLS estimator

ECO375F - 5.3 - Law of Large Numbers

ECO375F - 5.3 - Law of Large Numbers

ECO375F - 5.1 - Definition of consistency

ECO375F - 5.1 - Definition of consistency

ECO375F - 5.2 - Useful facts about plims + continuous mapping theorem

ECO375F - 5.2 - Useful facts about plims + continuous mapping theorem

ECO375F - 3.3 -Partialling Out Approach Example

ECO375F - 3.3 -Partialling Out Approach Example

ECO375F - 4.1 - Multiple Linear Regression: Conditional Variance

ECO375F - 4.1 - Multiple Linear Regression: Conditional Variance

ECO375F - 3.2 - Multiple Linear Regression: Unbiasedness

ECO375F - 3.2 - Multiple Linear Regression: Unbiasedness

ECO375F - 3.1 - Multiple Linear Regression: Partialling Out Approach

ECO375F - 3.1 - Multiple Linear Regression: Partialling Out Approach

ECO227 - Midterm 2013 - Q1.4

ECO227 - Midterm 2013 - Q1.4

ECO227 - Midterm 2013 - Q1.3 - Proof of the Binomial Theorem

ECO227 - Midterm 2013 - Q1.3 - Proof of the Binomial Theorem

ECO227 - Midterm 2013 - Q1.2 - Pascal's Identity

ECO227 - Midterm 2013 - Q1.2 - Pascal's Identity

ECO227 - Midterm 2013 - Q1.1 - Combination example

ECO227 - Midterm 2013 - Q1.1 - Combination example

ECO375F - Exam Solution 2014 Mideterm - Question 1 (OLSE)

ECO375F - Exam Solution 2014 Mideterm - Question 1 (OLSE)

ECO375F - 2.6 - Variance of the Slope Estimator (β1)

ECO375F - 2.6 - Variance of the Slope Estimator (β1)

ECO375F - 2.5 - Important Variance and Covariance Rules

ECO375F - 2.5 - Important Variance and Covariance Rules

ECO375F - 2.4 - Unbiasedness of the Intercept Estimator (β0)

ECO375F - 2.4 - Unbiasedness of the Intercept Estimator (β0)

ECO375F - 2.3 - Unbiasedness of the Slope Estimator (β1)

ECO375F - 2.3 - Unbiasedness of the Slope Estimator (β1)

ECO375F - 2.2 - Unbiasedness and Law of Iterated Expectations (LIE)

ECO375F - 2.2 - Unbiasedness and Law of Iterated Expectations (LIE)

ECO375F - 2.1 - Simple Linear Regression Model Assumptions

ECO375F - 2.1 - Simple Linear Regression Model Assumptions

ECO375F - 1.0 - Derivation of the OLS Estimator

ECO375F - 1.0 - Derivation of the OLS Estimator