62. TEN CLRM ASSUMPTIONS | Classical Linear Regression Model Assumptions | (10 important ticks )

#clrm #clrmassumptions #10assumptionsclrm
The classical Linear regression model is one of the important cornerstones of econometrics. It is important to understand the concepts and get hold of important areas.
The estimators that we create through linear regression give us a relationship between the variables. However, performing a regression does not automatically give us a reliable relationship between the variables. In order to create reliable relationships, we must know the properties of the estimators and show that some basic assumptions about the data are true. One must understand that having a good dataset is of enormous importance for applied economic research.
The estimation and hypothesis testing are the twin branches of statistical inference. Based on the OLS, we obtained the sample regression, such as the one shown in Equation.
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Thanks....
It's really important for understanding of CLRM properly...👌👌

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Thanks you very much ma'am you explained it very well.

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Thank you madam nice explanation

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does stochasticity of x to ensure linearity in clrm is sufficient or necessary?? mam pls reply

[@naumankhan1551]

the lecture is good but the assumptions should be explained with economic or non-economic variables examples in order to make it more easy for non econometrics students

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Hello Mam, can you do a video on criticism of all assumptions of the CLRM please!!!!

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[@abdallahalrawashdeh4746]

Can you explain what is bias please and what it does to a model !

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Hello Abdallah, bias is a situation where you don't get your required value of a statistic , it's a problem to any regression model. For more details feel free to contact us, mail ID: [e-mail]. Stay tuned for more such interesting topics. Subscribe and press the bell icon to never miss any update from Economics Pedia. Thank you very much!

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Hi really nice explanation

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Can you send me all links of full econometrics model

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aswm

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[@tejasviyadav2497]

is simple linear regr model and classic linear reg model the same thing

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Kya item ho

[@navyarishumishra]

I think minding your language would be better? This is what you have learned all your life?

[@chaturvediyash999]

No need to learn all these... Only 5 are main and important though...
Linearity
Independence
Homoscedasticity
Residual normality
No multicollinearity

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Thanks

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Thanks