# Search results

1. ### Dropping the blocking factor from models if it's not significant?

I don't think it would be good to drop the blocking factor. The blocking design presents a restriction on the randomization. That is, you randomize treatments within the blocks. So, I would think dropping the block would lead to biased results. In a subsequent experiment would wouldn't consider...
2. ### Accuracy of Regression Results?

I haven't used the data analysis in excel, but you would look at residual plots to check for nonconstant variance, normally distributed error terms and other model adequacy checks.

What is Jun?

I don't think it would fall under a factorial design since you don't have every level of one factor combined with every level of the other factor. Perhaps you could consider "The NRIA and NRCFC groups experienced the .2ma while the NR1a and NRC1a got the .8ma shock." as four treatments in a...
5. ### What to add to Binominal Regression? Demographics + motivations?

Well, as you add predictors to the model whether they are good/bad the r^2 value will always increase. I would test whether the predictors have a statistical significance with the response.
6. ### Expected Value of Insurance Payment

same as the original poster. I did something wrong. Don't know what.
7. ### Expected Value of Insurance Payment

I'm also interested in this question. I had thought: E(I) = 0 * P[d is in (284,532)] + (E(D) - 532) * P[d is in (532,1410)] but this doesn't result in any of the answer choices. So, I don't know.
8. ### Die With Multiple Same Sides

So, to get a sum of 3 you would need a 2 from die A and a 1 from die B. That probability is (2/6)*(1/6). Because of independence of the dice.
9. ### Die With Multiple Same Sides

First, I would define the random variable of interest. Then find all of the possible value that the random variable can take (and how these values come about). The part in parenthesis might help you think about the probabilities. Then use the definition of expectation and variance.
10. ### Probabibility

^ We can also use bayes to prove reincarnation or some bologna like that.
11. ### Two factor factorial and regression models

I'm making up this scenario for illustration of my question. Let's say I have factor A: low level 10 high level 30 And factor B: low level 5 high level 15 I have a quantitative response of interest. I can choose to compare means in the typical design setting. Or I can construct two regression...
12. ### Using predict function in R

Hello, I fit a model based on 150 some observations. I am having trouble with the syntax of the predict function. I've spent a few hours with this silly thing and I have a headache. Anyway, I have 4 explanatory variables call them x1,x2,x3,x4 I have 10 different combinations of the...
13. ### UMVUE

Thank you for helping me study. I have an exam on this material soon. :)
14. ### UMVUE

For any estimator A of theta, Bias(A)= E[A] - theta. So, when you take the expected value of your statistic you probably get (1/n) + theta. So, to make the estimator A unbiased subtract (1/n). That is, A - (1/n) is unbiased for theta. I believe Rao-Blackwell says this sufficient statistic is UMVUE.
15. ### minimal sufficient statisticc

Double Exponential I think. Although, it doesn't have to be a common distribution to be an exponential family.
16. ### Why does excel use 2 opposite ways for probability functions

The t distribution is bell shaped and symmetric. So, if you type in 0.95 with the appropriate df's you'll get the same critical value with an opposite sign. The F distribution is from 0 to infinity. So, what you input in the function will change depending on the test.
17. ### Null Hypothesis

So, what you're saying is you have some p values and you want to tailor your hypothesis to fit them? At least that's what it sounds like.
18. ### Immortality & Bayesian Statistics

From my understanding, the prior probability/distribution is assigned based on previous data/experiments. And the statistician would consult previous literature or talk with an expert in the field about this. And since none of this really exists in this case I haven't read much into this post...
19. ### How serious are violations of regression assumptions

what i mean is that you wouldnt know about nonconstant variance which might suggest a transformation to the response/predictor
20. ### How serious are violations of regression assumptions

How would you know about the form of the model if you don't look at a scatter plot or residuals vs fitted?