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True or False. Using heteroskedastic data will still provide for an unbiased estimator if assuptions a1...

True or False. Using heteroskedastic data will still provide for an unbiased estimator if assuptions a1 through a4 continue to hold.

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Answer #1

answer:

these is (true)

reasons :

  • In measurements, a gathering of arbitrary factors is heteroscedastic if there are sub-populaces that ...
  • Hence, relapse examination utilizing heteroscedastic information will even now give a fair-minded gauge to the connection between the ...
  • laid out above can be adjusted for utilize notwithstanding when the information don't originate from a typical appropriation.
  • A measurement is said to be a fair-minded gauge of a given parameter when the mean of the examining dispersion of that measurement can be appeared to be equivalent to the parameter being evaluated. ... s²
  • figured on an example is an impartial gauge of the fluctuation of the populace from which the example was drawn.
  • Heteroscedasticity implies unequal diffuse. ... Heteroscedasticity is an issue since customary slightest squares (OLS) relapse expect that all residuals are drawn from a populace that has a consistent fluctuation (homoscedasticity).
  • Heteroscedasticity is a hard word to articulate, however it doesn't should be a troublesome idea to get it.
  • Put essentially, heteroscedasticity (additionally spelled heteroskedasticity) alludes to the situation in which the inconstancy of a variable is unequal over the scope of estimations of a second factor that predicts
  • so these reasons based this istrue.
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