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  1. Buckeye

    Multivariate Regression

    You can get the beta hat values from the lm function. Or you can use knowledge of linear algebra to create an X matrix and a Y vector. Then you can do matrix multiplication following the formula from my previous post.
  2. Buckeye

    Multivariate Regression

    In matrix form: These are the parameter estimates that the lm function gives. X is the design matrix and y is the response vector. taken from: https://en.wikipedia.org/wiki/Linear_least_squares
  3. Buckeye

    z-score used as X axis variable

    Can you post a picture of the graph?
  4. Buckeye

    After performing an ANOVA, if it fails to reject H0 how we re-arrange the model?

    I think you should report the results as is. Maybe there is another model that is more suitable. But perhaps that should be left for a different analysis.
  5. Buckeye

    Difference between 2 dice rolls

    Think about it in terms of rolling one die twice. There is only one way to roll a 1 on the first and a 1 on the second. But i can roll a 2 and then a 1 or a 1 and then a 2. The other way to think about it is the dice are indistinguishable.
  6. Buckeye

    Ratio of two random variables squared

    So, it would be F (1,1) I think.
  7. Buckeye

    Ratio of two random variables squared

    If you divide the numerator RV by its degrees of freedom (n) and the denominator RV by its degrees of freedom (d), the result is F (n,d)
  8. Buckeye

    Fleeting/Random Thoughts

    My program has a theory course where we studied probability and statistical inference over two semesters. I have a strong math background, but we never really focused on by-hand integration/calculus beyond a bivariate distribution. As far as courses that I have benefited from, linear algebra is...
  9. Buckeye

    Design help

    De gin helps
  10. Buckeye

    High values for independent variables

    Yes, you just change the interpretation based on a unit change in the independent variable. For instance, if you left it as 1,200,000,000 you would be interpreting the mean change in y for a dollar increase in x. Versus the mean change in y for every billion dollar increase in x. There are a...
  11. Buckeye

    High values for independent variables

    Maybe instead of 1,200,000,000 you can use 1.2 billion?
  12. Buckeye

    Mapping variables

    maybe look up 'boolean algebra' in excel? Specifically, the 'and' operator.
  13. Buckeye

    urgent help required - data analysis of time course

    Is this more of an exploratory approach? Would the p values be smaller than they should be?
  14. Buckeye

    Help with effects statements

    From my understanding if you have the estimated beta coefficients of a regression model the overall anova effect is twice that value. Put another way, if you have an arbitrary effect the estimated coefficient is (effect)/2 I hope that's what you're asking. This assumes your predictors are coded...
  15. Buckeye

    Probability question to be calculated in R

    Seems right except that the standard deviation should be sigma/sqrt(n) But you see how the blue curve has a smaller spread. Which suggests that the chance of seeing a sample of 16 tvs with an average less than 4500 is quite low. Just remember that for a sampling distribution: 1.) randomly...
  16. Buckeye

    Probability question to be calculated in R

    The question is asking about the average lifetime of a random sample of 16 TVs. So, not every television will have a lifetime below 4500 hours. The sampling distribution plots averages of random samples of size 16 (over and over). If you plot a normal distribution with mean 4800 and standard...
  17. Buckeye

    Probability question to be calculated in R

    As the sample size increases, the standard error of the sampling distribution gets smaller. So, there is less variability around the mean. If you want to see visually, plot the density curves with different sample sizes.
  18. Buckeye

    Significant interaction no significant comparision

    I've always been taught to not interpret the main effect if there is a significant interaction. Perhaps there are other opinions.
  19. Buckeye

    Significant interaction no significant comparision

    Can you clarify what "I split my data by one variable and analyze the main effect by the other variable." means? I look at multiple comparisons using Tukey adjusted p-values in SAS. When there is significant interaction don't analyze the main effect.
  20. Buckeye

    minimal sufficient statistics

    Casella and Berger: Statistical Inference