# Search results

1. ### Statistical inference problem - complete sufficient and UMVU estimator

I would first find the joint distribution of X1,X2,X3 and then use the definition of information to find part a)
2. ### Experimental Design Help

I like to think of the situation where i have 2 treatment medications for dogs. Let's say to treat fleas. If I know the dog breed, I can block by breed. One of the ideas of a block is that it makes the EUs within a block more homogenous (and thus explains variability). The block is a restriction...
3. ### Testing if the averages between two independent data are closer compared to two other data

If you are comparing the same response and datasets 1 through 4 differ in the way of an explanatory variable. You could do one way anova maybe. and do a tukey adjustment for multiple comparisons
4. ### 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.
5. ### 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
6. ### z-score used as X axis variable

Can you post a picture of the graph?
7. ### 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.
8. ### 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.
9. ### Ratio of two random variables squared

So, it would be F (1,1) I think.
10. ### 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)
11. ### 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...

De gin helps
13. ### 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...
14. ### High values for independent variables

Maybe instead of 1,200,000,000 you can use 1.2 billion?
15. ### Mapping variables

maybe look up 'boolean algebra' in excel? Specifically, the 'and' operator.
16. ### 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?
17. ### 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...
18. ### 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...
19. ### 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...
20. ### 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.