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Econometrics and Analytics
Приєднався 20 вер 2015
Business analytics II - Week 7 - 04 Designing Conclusive Experiments
Business analytics II - Week 7 - 04 Designing Conclusive Experiments
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Відео
Business analytics II - Week 7 - 03 Split Testing and A B Tests
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Business analytics II - Week 7 - 03 Split Testing and A B Tests
Business analytics II - Week 7 - 02 Preparing for Experiments
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Business analytics II - Week 7 - 02 Preparing for Experiments
Business analytics II - Week 7 - 01 Experiments and Causality Issues
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Business analytics II - Week 7 - 01 Experiments and Causality Issues
Business analytics II - Week 6 - 06 Checking Components of Time series
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Business analytics II - Week 6 - 06 Checking Components of Time series
Business analytics II - Week 6 - 05 Cycles
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Business analytics II - Week 6 - 05 Cycles
Business analytics II - Week 6 - 04 Seasonality
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Business analytics II - Week 6 - 04 Seasonality
Business analytics II - Week 6 - 02 Time Series Visualization and Smoothing
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Business analytics II - Week 6 - 02 Time Series Visualization and Smoothing
Business analytics II - Week 6 - 01 Introduction to Time Series and Forecasting
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Business analytics II - Week 6 - 01 Introduction to Time Series and Forecasting
Business analytics II - Week 6 - 03 Time Series Components and Trends
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Business analytics II - Week 6 - 03 Time Series Components and Trends
Business analytics II - Week 5 - 06 Out of Sample Validation
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Business analytics II - Week 5 - 06 Out of Sample Validation
Business analytics II - Week 5 - 05 About Best Subsets and Stepwise Regression
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Business analytics II - Week 5 - 05 About Best Subsets and Stepwise Regression
Business analytics II - Week 5 - 04 Best subsets Regression (with R)
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Business analytics II - Week 5 - 04 Best subsets Regression (with R)
Business analytics II - Week 5 - 03 Stepwise Regression (with R)
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Business analytics II - Week 5 - 03 Stepwise Regression (with R)
Business analytics II - Week 5 - 02 Multicollinearity
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Business analytics II - Week 5 - 02 Multicollinearity
Business analytics II - Week 5 - 01 Model Building Considerations
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Business analytics II - Week 5 - 01 Model Building Considerations
Business Analytics II - Week 4 - 07 Non Linear Transformations
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Business Analytics II - Week 4 - 07 Non Linear Transformations
Business Analytics II - Week 4 - 06 Linear Transformations
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Business Analytics II - Week 4 - 06 Linear Transformations
Business Analytics II - Week 4 - 05 Interaction Terms
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Business Analytics II - Week 4 - 05 Interaction Terms
Business Analytics II - Week 4 - 04 Categorical Variables
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Business Analytics II - Week 4 - 04 Categorical Variables
Business Analytics II - Week 4 - 03 Model Comparison and Adjusted R2
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Business Analytics II - Week 4 - 03 Model Comparison and Adjusted R2
Business Analytics II - Week 4 - 02 Fitted and Incremental Values in Multiple Regression
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Business Analytics II - Week 4 - 02 Fitted and Incremental Values in Multiple Regression
Business Analytics II - Week 4 - 01 Introducing Multiple Regression
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Business Analytics II - Week 4 - 01 Introducing Multiple Regression
Business Analytics II - Week 3 - 10 Regression: Assumptions
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Business Analytics II - Week 3 - 10 Regression: Assumptions
Business Analytics II - Week 3 - 09 Regression: Residuals vs Population Errors
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Business Analytics II - Week 3 - 09 Regression: Residuals vs Population Errors
Business Analytics II - Week 3 - 08 Regression: Practical Significance
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Business Analytics II - Week 3 - 08 Regression: Practical Significance
Business Analytics II - Week 3 - 07 Regression: Confidence Intervals
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Business Analytics II - Week 3 - 07 Regression: Confidence Intervals
Business Analytics II - Week 3 - 06 Regression: p-values
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Business Analytics II - Week 3 - 06 Regression: p-values
Business Analytics II - Week 3 - 05 Regression: Interpreting Coefficients and Standard Errors
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Business Analytics II - Week 3 - 05 Regression: Interpreting Coefficients and Standard Errors
Business Analytics II - Week 3 - 04 Regression: Causality
Переглядів 26Рік тому
Business Analytics II - Week 3 - 04 Regression: Causality
Thank you
Thank you for this
GREAT
7:21 Did you mean Yi here? Instead of Y1
Is the sum of the error term the same as the expected value of the error term, since both equal zero?
6:52 Is the bar notation here the same as E[], i.e., the expected value?
Excellent presentation and clear! Thanks. Is there a way of including AIC?
My listening skill is not well, please adding the subtitle so that i can understand it more
Thank you so much! Merci infiniment pour votre clarté absolue.
Could you please do this in matrix form?
Why did you assume \sum(x_i - x_bar) = 0 in @5:00 but then take a conditional expectation of the same variables in @8:00 ? I would have just assumed they both equal to zero and have the \sum(xi-x_bar)^2 cancel. You would be left with beta_1_hat = beta_1. Great video either way. I am just not sure why you didn't assume \sum(x_i - x_bar) = 0 for both cases.
I am so upset for finding your videos late
I dont know what I am gonna pass the exam
Same mate, good luck xD
Wait, after we get sum z_i*(y_i - y-bar) = n_1(y-bar_1 - y-bar), we can similarly get sum z_i*(x_i - x-bar) = n_1(x-bar_1 - y-bar) - Why don't we just cancel out n_1? In this way we will get beta = y-bar_1 - y-bar / x-bar_1 - x-bar. This must be wrong (because otherwise y-bar_0 = y-bar and x-bar_0 = x-bar), but I don't see why we cannot cancel out n_1 at this stage.
To find Beta 2 Hat is it the exact same method as finding Beta 1 Hat? But Step 1 would instead be: xi2 = a0hat + a1hat * xi1 + ri2hat
Good explanation and video!!
this is top tier explanation
the last section of 'Note', why we take the mean value instead?
Thank you so much!!!!
Bro have an exam tomorrow and you made it super simple to revise...! thank you
Why Yi hat does not include ui hat
Hey I think I got lost from 6:36 onwards
Really clear explanation. Thanks man!
Liked and fully watched!! This channel desperately needs smzeus!!
Why the sum of (xi-x(bar)) is constant? Here, xi takes different values!! And why you take conditional expectation?
Its a good presentation
Can you tell me between 9 and 17 minutes please
Could you please tell me after the 9th minute?
why the residual of partialling out not equal the residual of ols ,whereas the residual of fwl = residual ols
You teach really well! Thanks a lot!
Maybe it is better to mention upfront that you have assumed X as stochastic in the proof.
Thank you so much for the amazing video. The book completely skipped this explanation part which drove me crazy. Finally understood with your explanation.
I still don't get it I guess I'll watch it 10 more times :(
If it is not clear, maybe you can be more effective by covering the prerequisites instead of rewatching it. To understand it, you need 2 things: calculus and optimization. Maybe watch some videos about these topics first, then the video should become easy to follow.
@@RemiDav Ok thank you
This has contributed a lot to my understanding. Thank you
You legend, I hope you're still teaching.
Thank you so much for this, much easier to understand than my lecturer 👍
Amazing video g
CAN'T BELIEVE THIS WAS SO SIMPLE
Thanks for showing this derivation step by step!
Anyone from ETC 2410 here? 😅
Just wanted to say that I really appreciate all your efforts for putting these up, it's helping students even 7 years later, thank you :)
Thanks a lot
thank you, really helpful
How do we prove unbiasedness of b0?
Thank you for taking the time and making this video.
Thank you thank yo thank you🙈
At 1:05 I thought that the numerator was Σ(x-xbar)(u) not Σ(x-xbar)(u-ubar), how did you get that?
Σ(x-xbar)(u) is the same as Σ(x-xbar)(u-ubar). It's the same logic as Σ(xi-xbar)(yi -yibar) = Σ(xi-xbar)(yi) shown in video 2.3
Thank you so much!
How do you prove the first implication with the mean of the square.
Define a new variable z = y^2, and apply the law of large numbers on z to get E(z), which is E(y^2)
Excellent Tutorial.