Simple Linear Regression MLE are the same as LSE

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In this video I show that under the normality assumption for the model error, Simple Linear Regression Maximum Likelihood Estimators are the same as Least Squared Estimators

Stats4Everyone

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Very very helpful.... Thanx.... I was just unable to catch up with the pace of professor in the linear models... Your playlist really helped me a lot ... Thanx again ❤️

shahbazahmad

thanks a lot, life saver before the stats exam

judhajeetchoudhuri

Thank you ma very much for this video. You made my day.

ezraezekielemmanuel

Thank you very much for this video. However, I think the Yi's are independent but not identically distributed since they have different expected values. Despite viewing the Xi's as constant, we change these values from one y value to another. What do you think about this?

elviswanasunia

Thank you very much for this video. I just had one doubt. Don't we take ln after L in MLE? Or is it not required here?

aditshrimal

Can you please explain why y will have normal distribution?

RaviShankar-jmqw

Hey, isn't it wrong to assume that x_i are non-random? What I understand is that they ARE random and we can only say that y_i is unconditionally normal if x_i and y_i are jointly normally distributed

Counter

Can you explain why did you say 'these are residuals' at 7:50, when they are epsilon/error?

where Yi = beta_0 + beta_1 * Xi + Error
and Y_hat = beta0_hat + beta1_hat * Xi

Just asking. Bit confused.

AbhinavSingh-oqdk

Simple Linear Regression MLE are the same as LSE

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