Видео

Is these any reason that you are taking beta as k*1 vector when we are having k features(x1,x2,...xk). While no assumption on standadization has been taken, how you ommited beta_0 ie intercept term?

I did not get how linear models are first-degree parameters and not variables. I always thought that linear models would have variables as first-degree components as anyways the parametric values are constants in a way.

You are genius sir ❤ you clear my whole doubt in modeling. Thanks a lot🙏😊

ty very much sir

21:03

how cross product term is zero ?

If there is slight fluctuation of the skew of the matrices formed by the Betas and the residuals then shall we take the mean or the variance to take an optimum to the decisisive of fit.

So for the residual normal curve we see the constancy over time as the Normalized theory on the SE and the non distributed for diff n.

Sir the zeroes calculation,the approach of limiting a polynomial always suggests a point but the drection of the variant of polynomial. Thus all become philosophical,which one should be perfect?

Sir is there any possibility of large polynomial time function of multivariate?measuring the Beta to any n,could that be hard enough to ML method?

For a production function the most folowed situation is the E to the power of n of any Beta,but what if on a randomized run the 3rd repeation gives randomized and some cases the constancy in case of the total output,hence which one will be a factor of Coeff of adjustment or the corr coeff?

Supposing a case for the linerity between the Mean time to testing of a big industry doesnt follow the Chi square distributed portion but the posterior portion where Expectation follows negative correlatedness with the temporality with the SE of the MLE at the Chi squared point hence would it be a great aversion for type 1 error while the testing is randomized?

Regression when comes marginal,is that suffient to draw inference on Beta next run of an industrial output having high MTTF to regain the regression without the formation of inverse Eigen and other derivative testing?

but how E(y)=B1X1+B2X2+..... Why didn,t you use expectation in variable?

Why Expectation of y E(y)is =B°+B1X? Why not we use E(x)?

But why beta^2 is linear!! Suppose, Beta^2=beta1!!

👍👍

Great sir

Tq

Good easy video

Can R Squared be used in the case of non-linear Regression?

shit explanation my recomendation is not to watch this shit video

Thank you sir

What are the slop of the regression line equation for 3 or more independent variable?

Thanks a lot for easy explanation.

Thanks a lot for mathematical explanation.

why we are dividing SSres by sigma square in 11:50

There is a mistake in the test statistic for variance, since there should not be the (n-2) term in the distribution. The distribution of SSres/sigma^2 is chi- square with (n-2) degrees of freedom. Kindly clarify on that .. Thank you

Thank you sir☺

Amazing ...👌 thank you

Such a boring video

Thanku so much for the lecture ... I've been searching for this from 2 days ..now I'm.able.to understand the concept 🙏 😁

Thank you so much sir heartily saying your vedeo made me happy a lot and contented me ...really how much happy i can't express..i was freting much by taking such a subject like econometrix..but with in a blink of an eye you faded my all fret and concern about econometrix..such a awesome video it is..🙏🙏you are great sir..

Nice work sir

This man is on some GOAT level

Why prof are boring

thank you sir, it is very much helpful to understand

Ur drawing coming under subtitles, please correct it.

Plz show the 1st and 2nd lecture ,sir

Can anyone please enlighten me why var(CiYi) is expanded in that form at around 14:50.

Very good

Changi edye antha comment madiroru yalla Nim students irbayku

Ogo lo yanu artha agilla

Bravo Professor keep it going

Thank you sir

Thank You Sir, excellent elaboration of basic concepts in mathematical model & parameters

very good explanation Sir...Thank you.

Thank You Sir Clear explanation

How var(ybar)=sigma square /n. Their should be n square.

clear and very well explained thank you