I have variance of response. How can I find it's MSE?

In summary, the person is asking for help understanding the relationship between y and the ANOVA MSE in their regression model. They also mention using R software and suggest using lm() to set up the model.
  • #1
HF08
39
0
To All,

I did a study and my response is defined as y = b1x1 + b2x2 + e where e ~N(0,1).
I have y~N(4,33). In my data results, I did an ordinary least squares regression model
for y = b1x1+b2x2+ e. The ANOVA is telling me the mean of y is 4, but MSE is 1.

So here is my question. If I know y~N(4,33). How can I determine the ANOVA MSE
is going to be 1?

Thanks,
HF08
 
Physics news on Phys.org
  • #2
I think you are getting a bit confused here. [tex]\underline{y}=X\underline{\beta} + \underline{\epsilon}[/tex] is your model? There is no way that [tex] y [/tex] could be [tex] N(4,33) [/tex] when your [tex] \epsilon_i [/tex] are [tex] N(0,1) [/tex]. Also that ANOVA does not make sense if you have [tex] \epsilon_i [/tex] as there is no variance to analyse. In this case ANOVA is right. As
[tex] Var(y_i)=Var(\mu_i+\epsilon_i)=Var(\epsilon_i)=1 [/tex].


What sofware are you using for this? Try using R. Don't bother with saying that [tex] \epsilon_i [/tex] are [tex] N(0,1) [/tex] unless you have a very good reason to know this. Usualy [tex] \epsilon_i [/tex] are [tex] N(0,\sigma^2) [/tex] where you will have to estimate [tex] \sigma^2 [/tex]. In R the model you have can be set up by lm(y~b1+b2-1,data=dataname).

Hope this helps
 

Related to I have variance of response. How can I find it's MSE?

1. What is variance of response?

Variance of response is a measure of how much the data points in a sample vary from the mean. It is a statistical concept used to understand the spread or dispersion of a set of data points.

2. How is variance of response calculated?

Variance of response is calculated by taking the sum of the squared differences between each data point and the mean, divided by the total number of data points in the sample.

3. Why is it important to find the MSE of variance of response?

MSE (Mean Squared Error) is a measure of the average squared difference between the actual data points and the predicted values. It is important to find the MSE of variance of response to evaluate the accuracy of a statistical model and make adjustments if necessary.

4. What are the uses of MSE in relation to variance of response?

MSE can be used to compare the performance of different regression models, as well as to assess the overall fit of a model. It can also be used to identify influential data points or outliers in a dataset.

5. Are there any limitations to using MSE for variance of response?

One limitation of using MSE is that it treats all errors equally, regardless of their direction. This means that it may not accurately reflect the performance of a model if the dataset contains a large number of outliers or extreme values. In addition, MSE can be heavily influenced by the sample size and the number of predictors in a model.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
4
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
9
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
849
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
23
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
998
Replies
8
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
17
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
11
Views
1K
Back
Top