# Search results

1. ### Expected value of chi-square distribution

X_1 and X_1 are not independent so E(X_1**2) does not = E(X_1)*E(X_1)

3. ### Why does bootstraping with 50% samplesize and no replacements always gives appropriate confidence intervals.

Interesting problem, though.
4. ### I have a Mean and Standard Deviation. If I multiply the Mean by a number (non-constant), do I have to multiply the Standard Deviation by that number?

A little, but I have no intuitive feel for this foreign and statistics intense game. All I can suggest is that you try both ways and see which has more success.
5. ### Why does bootstraping with 50% samplesize and no replacements always gives appropriate confidence intervals.

You're right - the jackknife should strictly use the means of all the subsamples. However, if there are too many to enumerate, then you will get a good approximation by taking a random sample of the subsample means. If, for instance, you have a sample of 50 and you consider all the subsamples of...
6. ### I have a Mean and Standard Deviation. If I multiply the Mean by a number (non-constant), do I have to multiply the Standard Deviation by that number?

I think we are talking at cross purposes here. The short answer, in my opinion, is that you can decrease the numbers to 85% and the variability will automatically decrease to 85% without you doing anything, or you can use the untouched numbers and decrease the mean and SD at the end. If this...
7. ### I have a Mean and Standard Deviation. If I multiply the Mean by a number (non-constant), do I have to multiply the Standard Deviation by that number?

Yes Do an experiment in Excel. Type some random numbers. Find the mean and SD. Multiply the numbers by 0.85. Find the mean and SD. Compare the means and SDs
8. ### Why does bootstraping with 50% samplesize and no replacements always gives appropriate confidence intervals.

I wouldn't dismiss the jackknife too quickly. The delete-d jackknife method has a factor ( n-d)/d which is just 1 in your case. The only real difference is that you are taking a random sample of possible deletions instead of listing them all.
9. ### Why does bootstraping with 50% samplesize and no replacements always gives appropriate confidence intervals.

Is this something to do with the jackknife?
10. ### Descriptively nterpreting data output

Typically you get a slope and an intercept. Often the slope has a real-world meaning such as a rate and means how much y increases for each unit increase in x. Sometimes the intercept also has a real-world meaning as a zero or starting level but this is less common.
11. ### probability density

If you know the form that your data takes, (normal say) then StatsSolver's suggestion is a good one. If you want the PDF of your actual data and you don't know the form, then a histogram of the values is a PDF once it has been scaled for an area of 1. Say you have 2000 readings and you make a...
12. ### Is this a regression?

When you have found a way to do it, what will the outcome look like?

Cheers. Kat
14. ### Normality test in data with multiple treatments

The normality applies to the residuals, not the data itself. Many folk are happy with looking at a normal plot of all the residuals as a group. If there is a strong pattern away from the straight line, then this may indicate that the model can be improved with a transformation, say, rather than...
15. ### Hypothesis testing for 2 sample proportion

What is A/B testing? Also, this doesn't seem like proportions because neither 67.3% of 9 nor 78.2% of 12 are whole numbers.
16. ### Design of Experiments with existing data

The coefficients of X1 and X1sq together define the parabola. Between them you can determine the X1 value that gives minimum (or maximum) Y. I have no idea what Y is in your experiments but if it something that is good when it is low (like Y is cost or curing time or resistance) then your...
17. ### Design of Experiments with existing data

Oddly, no. It is still technically a linear regression. See Spunky's recent post at http://www.talkstats.com/threads/linear-vs-non-linear-functions.77498/#post-229519 You have four columns of predictor variables (including X1sq). The response Y = constant + A.variable 1 +B.variable 2 +C.variable...
18. ### Two-way partially crossed design - model selection and inference

You could try a GLM. Usually you can nominate the interactions you want. Second thoughts, probably not, because there would be missing data.
19. ### Design of Experiments with existing data

Regression assumes that all the relationships are straight lines. Equal increments to a variable produce equal increments to the response. However, as we increase X1, at first Y goes down until X1 is about 100 and after that Y starts to increase. The X1sq term make the straight line into a...
20. ### Design of Experiments with existing data

OK. You really need both the X1 and the X1squared terms in the model. Something like this. Note the minimum about 100. Both the X1 and the X1squared terms are significant.