Give an example to show that if not assuming independence of

In summary, assuming independence means that the occurrence of one event does not affect the occurrence of another event. An example of this is flipping a coin, where each flip is considered to be independent of the others. Not assuming independence can make statistical analysis more complex and may lead to misleading results. To account for lack of independence, methods such as using multilevel or longitudinal models or conducting sensitivity analyses can be used.
  • #1
squenshl
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Give an example to show that if not assuming independence of X1, X2, ..., Xn it is possible to show that Var(1/n * sum from k = 1 to n of Xk) >> [tex]\sigma^2/n[/tex]
 
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  • #2


What do you get if [itex]X_1 = X_2 = ...[/itex], which is sort of the EXTREME example of non-independence?
 
  • #3


Since X1 = X2 = ... = Xn.
This implies that Var((1/n)*nX1) = n2[tex]\sigma^2[/tex]/n2 = [tex]\sigma^2[/tex] >> [tex]\sigma^2/n[/tex]
 
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What does it mean to assume independence?

Assuming independence means that the probability of one event occurring does not affect the probability of another event occurring. In other words, the two events are considered to be unrelated and do not influence each other.

Can you give an example of assuming independence?

One example of assuming independence is flipping a coin. The outcome of one coin flip does not affect the outcome of another coin flip. Each flip is considered to be independent of the others.

How does not assuming independence affect statistical analysis?

If independence is not assumed, statistical analysis becomes more complex and may require different methods. This is because the assumption of independence is often necessary for certain statistical tests to be valid.

Can you give an example to show that if not assuming independence, results may be misleading?

An example of this is conducting a survey on the relationship between smoking and lung cancer, but not considering other factors such as genetics or exposure to secondhand smoke. If these factors are not taken into account, the results may suggest a strong correlation between smoking and lung cancer when in reality, the relationship may not be as strong if independence is assumed.

How can we account for lack of independence in statistical analysis?

There are various methods for accounting for lack of independence in statistical analysis, such as using multilevel or longitudinal models, or conducting sensitivity analyses to assess the impact of the lack of independence on the results.

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