Normality is imp to make valid inferences--especially in small samples--in large samples one can rely on Central limit theorem and can ignore normality
If u look at analytical formula for the OLS estimates of true parameters then u wil realize that these are linear combination of true errors. By assuming normality of errors the inference regarding significance may b different. Since true errors r unknown and unobserved so usually residuals obtained from OLS regression are tested for normality
Great to see you back sir.
Thank you!!!
Very Informative😍
Thanks a bundle!!
Amazing lecture sir
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Thank you for the sharing
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After a very long break sir. ❤️❤️❤️
From now on I will be regular IA!
Sir ❤️
Thank you!!!
Murshad ❤️😁
Thank you!
Why normality tests? Why dont we implement poissonity test? What makes normal distribution privileged among other distributions?
Normality is imp to make valid inferences--especially in small samples--in large samples one can rely on Central limit theorem and can ignore normality
@@FAMFAMI firstly thank you for answer. Can you say all fusses about normality tests stem from trying to infer meaning from small dataset?
If u look at analytical formula for the OLS estimates of true parameters then u wil realize that these are linear combination of true errors. By assuming normality of errors the inference regarding significance may b different.
Since true errors r unknown and unobserved so usually residuals obtained from OLS regression are tested for normality