the relationship between standard of error estimate and the correlation coefficient r?
95550
r - standard error = the significance of the estimate
885975
Good lord. I had to teach myself statistics, and I don't remember a lot of it, but I'll do what I can.\n\na) The null hypothesis is that the two learning environments have no impact on the grades of the course. The alternative hypothesis is that they will be different.\nb) Two-tailed, since you do not know which environment will be better or worse.\nc) The time spent in one vs. the other, and the quality of the teacher (as measured as a variable.)\nd) That there would be a standard margin for disparity.\ne) I have no clue. I never got that far.\n\n2) Don't know.
120843
R^2 (root mean square) is just a statistical measure of how well your data lines up. Anything over .99 is near perfect.
189782
It's a calculation called the r value.  The link below shows how to calculate it.  If you take a college level statistics class you'll learn how to do it with a good calculator.
1011608
I really like R.  CRAN (the comprehensive R archive network) has packages that might help you out.\n\nTry a Yahoo Search such as:\ncanonical analysis CRAN\nhttp://search.yahoo.com/search?ei=utf-8&fr=slv8-wdgt&p=canonical%20analysis%20CRAN\n\nIt turns up results such as:\nhttp://www.bioconductor.org/CRAN/src/contrib/Views/Environmetrics.html\nhttp://www.bioconductor.org/CRAN/doc/packages/pcurve.pdf#search='canonical%20analysis%20CRAN'
909046
7.5\n\nd=rt so basically 7.5 is r
885608
Using a simple TI-83, the data in #3 gives a linear regression equation of\ny = 0.4668x - 31.737, where y is absences and x is temperature.  r = 0.977.\n\nSound like you've been partying too much and ignoring your classes.  Well, you get what you choose.
889826
Hi, \n\nI think you are referring to the r-squared value of an equation that models a set of data. Often times when we plot a set of data, we want to find a relationship between the points that reveals an underlying relationship. In some cases, that relationship is linear, that is all the points lie roughly on a line.\n\nThe R-squared value tells you how well your line fits the points that you've plotted. It's simply a way of judging how good your approximation is.
1156399
This is a good question even for a statistician.\nThe problem is your set is small and I do not know what the deviation between samples is, however the repeatability and reproducibility information can bind the window \nthrough which the third value will fall through or perhaps even remain in.  \n\nLet me know.
437525
When you compute a mean from a sample, you are estimating the mean of the population from which you sampled.  The population mean is sometimes refered to as the true mean and the mean you compute from your sample is called the sample mean.\n\nWith repeated sampling, you'll come up with a different sample mean each time.  This distribution of sample means is refered to as the sample distribution and it is approximately Normal in distribution.\n\nWhen you compute the standard error using the standard deviation and the sample size, you are estimating the variability in the sample mean.\n\nWhen you estimate confidence intervals, you are estimating the range of possible values where the population may lie relative to your sample mean.\n\nFor instance, your sample mean plus or minus 1.96 standard errors represents an estimate of the 95% confidence interval or range that may contain the population mean relative to your sample mean.  There is a 5% chance that the population mean is actually higher or lower than this range.\n\nLet's say you toss a coin twenty times and measure 9 heads and 11 tails.  The average number of coin tosses at 0.45 heads (which implies 0.55 tails).  The standard error estimate is 0.055343. Multipling the standard error estimate by 1.96 and adding and subtracting the result from the mean yields our 95% confidence interval. There is a 95% chance that the population mean for this process is between 0.341528 and 0.558472.
211268
Regression analysis is very complicated mathematical/statistic analysis that is very difficult to describe easily.  R-squared is a measure of how closely an investment tracks the S&P 500 or other index.  While I am not a great fan of investopedia.com, they give some answers in this case.\n(These comments refer to investment analysis, regression analysis is also used in other fields.)
766587
(1) The correlation answers the STRENGTH of linear association between paired variables, say X and Y. On the other hand, the regression tells us the FORM of linear association that best predicts Y from the values of X.\n\n(2a) Correlation is calculated whenever:\n* both X and Y is measured in each subject and quantify how much they are linearly associated.\n* in particular the Pearson's product moment correlation coefficient is used when the assumption of both X and Y are sampled from normally-distributed populations are satisfied \n* or the Spearman's moment order correlation coefficient is used if the assumption of normality is not satisfied.\n* correlation is not used when the variables are manipulated, for example, in experiments.\n \n(2b) Linear regression is used whenever:\n* at least one of the independent variables (Xi's) is to predict the dependent variable Y. Note: Some of the Xi's are dummy variables, i.e. Xi = 0 or 1, which are used to code some nominal variables.\n* if one manipulates the X variable, e.g. in an experiment. \n\n\n(3) Linear regression are not symmetric in terms of X and Y. That is interchanging X and Y will give a different regression model (i.e. X in terms of Y) against the original Y in terms of X.\nOn the other hand, if you interchange variables X and Y in the calculation of correlation coefficient you will get the same value of this correlation coefficient.\n\n(4) The "best" linear regression model is obtained by selecting the variables (X's) with at least strong correlation to Y, i.e. >= 0.80 or <= -0.80 \n\n(5) The same underlying distribution is assumed for all variables in linear regression. Thus, linear regression will underestimate the correlation of the independent and dependent when they (X's and Y) come from different underlying distributions.
674991
For one-sample t-test, \nt = (sample mean - population mean) / estimated standard error. \n\nIn math notation, t = (X bar - mu not) / (s/sqrt(n))\n\nAssumptions: (normal population or sample size, n, is at least 30) and population standard deviation is unknown.\n\nThere are other variations of t-tests like two-sample(pooled and unpooled) and paired ...
918974
Correlation! (Maybe... well the 6th letter is L at least).\n\nCorrelation basically describes the strength and direction of the (linear only) relationship between two variables or sets of observations. \n\nIt's a number between -1 and 1, and is denoted by 'r' or the Greek letter 'rho' (it looks like a 'p').  E.g. r = 0.87 \n\nI hope that's the kind of statistical symbols the crossword clue is referring to. \n\nGood luck with the crossword!
830991
I'm wondering why you created the data 1-3500?  I would think you would use the set of data available and figure the standard deviation and average.  With that information, you would be able to predict tomorrows value at a certain confidence level.  I forget is that the p test or the t test.  its been a while since I took statistics.
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If x and y have a strong negative linear correlation, r is close to -1.  An r value of exactly -1 indicates a perfect negative fit.  Negative values indicate a relationship between x and y such that as values for x increase, values for y decrease. \n\nSo I would say that x and y have a strong negative linear correlation.  \n\nTaking r^2 = 0.6724 = 67.24% of the total variation in y can be explained by the linear relationship between x and y.  The other 32.76% remains unexplained.
1109806
correlation coefficient i really couldnt tell you a formula, but there is a button on your graphing calculator that tells you it, correlation coefficient is a number from 0 to 1, stating how close a best fit line is to a series of points, 1 being perfectly on the line, so unless your dealing with a best fit line i have no idea...