Bias and Variance of Estimators for Population Mean

In summary, the conversation discusses three different estimators for the population mean, denoted as \mu 1, \mu 2, and \mu 3, and asks for the associated bias and variance for each one. The solution is found using the rules for expectations and variances of linear combinations of independent variables, and the results are given for each estimator.
  • #1
gutnedawg
35
0

Homework Statement


Suppose you have a random sample {X1,X2,X3} of size n=3. Denote the population mean [tex]\mu[/tex] = E(Xi) and the population variance [tex]\sigma[/tex]^2= VAR(Xi). Consider the following three estimators for [tex]\mu[/tex]

[tex]\mu[/tex] 1= X
[tex]\mu[/tex] 2= X1/5 +X2/2 +X3/5
[tex]\mu[/tex] 3= (X1+X2+X3)/5

What is the bias associated with each estimator?
What is the variance associated with each estimator?

Homework Equations


I'm not sure


The Attempt at a Solution



I'm really at a loss now.
 
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  • #2
hey guys,how do i post my questions
 
  • #3
gutnedawg said:

Homework Statement


Suppose you have a random sample {X1,X2,X3} of size n=3. Denote the population mean [tex]\mu[/tex] = E(Xi) and the population variance [tex]\sigma[/tex]^2= VAR(Xi). Consider the following three estimators for [tex]\mu[/tex]

[tex]\mu[/tex] 1= X
[tex]\mu[/tex] 2= X1/5 +X2/2 +X3/5
[tex]\mu[/tex] 3= (X1+X2+X3)/5

What is the bias associated with each estimator?
What is the variance associated with each estimator?

Homework Equations


I'm not sure


The Attempt at a Solution



I'm really at a loss now.

For each estimator use the rules for expectations and variances of linear combinations of independent variables.

[tex]
\begin{align*}
E(aX + bY) &= aE(X) + bE(Y) \\
Var(aX + bY) & = a^2 Var(X) + b^2 Var(Y)
\end{align*}
[/tex]

Try these with your estimators and show what you find.
 

1. What is bias probability?

Bias probability refers to the likelihood that a study or experiment has been influenced by a preconceived notion or prejudice, leading to inaccurate or skewed results.

2. Why is bias probability important in scientific research?

Bias probability is important because it can affect the validity and reliability of the data and conclusions drawn from a study. If bias is present, the results may not accurately reflect reality and can lead to inaccurate or misleading conclusions.

3. How can bias probability be minimized in scientific research?

Bias probability can be minimized by using rigorous research methods, having a diverse and unbiased sample group, and being transparent about any potential biases. It is also important for researchers to critically evaluate their own biases and actively work to prevent them from influencing the study.

4. What are some common types of bias in scientific research?

Some common types of bias in scientific research include confirmation bias, selection bias, and publication bias. Confirmation bias occurs when researchers only seek out evidence that supports their hypothesis, while selection bias occurs when the sample group is not representative of the population. Publication bias refers to the tendency for studies with positive or significant results to be more likely to be published than those with negative or insignificant results.

5. How can bias probability impact society?

Bias probability can have a significant impact on society as it can lead to misinformation, discrimination, and unequal treatment. In fields such as medicine and criminal justice, bias can have serious consequences for individuals and communities. It is important for researchers to be aware of bias probability and work towards eliminating it in order to promote fairness and accuracy in scientific research.

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