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  1. Buckeye

    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. Buckeye

    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.
  3. Buckeye

    Please help, which ANOVA should I use?

    What is Jun?
  4. Buckeye

    Please help, which ANOVA should I use?

    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. Buckeye

    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. Buckeye

    Expected Value of Insurance Payment

    same as the original poster. I did something wrong. Don't know what.
  7. Buckeye

    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. Buckeye

    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. Buckeye

    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. Buckeye


    ^ We can also use bayes to prove reincarnation or some bologna like that.
  11. Buckeye

    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. Buckeye

    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. Buckeye


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


    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. Buckeye

    minimal sufficient statisticc

    Double Exponential I think. Although, it doesn't have to be a common distribution to be an exponential family.
  16. Buckeye

    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. Buckeye

    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. Buckeye

    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. Buckeye

    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. Buckeye

    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?