In statistics, how can i tell when to use x-bar and when to use p-hat?
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well, first of all, p-hat is a proportion and x-bar is the arithmetic mean. \n\nYou always use the z-test for p-hat. If you're having trouble recognzing what a proportion is, think of it as percent. If you say, "15% of the flowers are purple" then the proportion is .15 (which could, for example, be 15/100). p-hat will ALWAYS be a number between 0 and 1.\n\nUse x-bar when you want to the test the mean. It's a little tricky to decide when to use a t-tets for a z-test and you professor might be more strict than others. I've linked a flow chart below which will help you decide when to use a t-test or z-test but here is a summary of the rules (from the website cited below):\n\n\nZ TEST\n\n       data points should be independent from each other\n\n       z-test is preferable when n is greater than 30.\n\n       the distributions should be normal if n is low, if however n>30 the distribution of the data does not have to be normal\n\n       the variances of the samples should be the same (F test) \n\n       all individuals must be selected at random from the population\n\n       all individuals must have equal chance of being selected\n\n       sample sizes should be as equal as possible but some differences are allowed\n\n\n\nT TEST\n\n       data sets should be independent from each other except in the case of the paired-sample t-test\n\n       where n<30 the t-tests should be used\n\n       the distributions should be normal for the equal and unequal variance t-test (K-S test or Shapiro-Wilke)\n\n       the variances of the samples should be the same (F-test) for the equal variance t-test\n\n       all individuals must be selected at random from the population\n\n       all individuals must have equal chance of being selected\n\n       sample sizes should be as equal as possible but some differences are allowed\n\n\n\nKeep in mind that in some case you may ahve to use a weighted t`. If you don't know what that is, you teacher might not have asked you to know that information.\n\nContact me if you need more info.
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Have you tried the ezplot routine?  You can get help from the context help window.  Mathlab is the bomb.
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As to part one, I can not recommend any acceptable, known test for such data (perhaps what I've written below might work).\n\nAs to part two, this is a horrid approximation since the midrange as the mean is a major assumption (even more far-reaching than assuming something about the populations' probability distributions).\n\nFor part 3 and also part 1:\n\nThe first problem you run into is that you do not know anything about the nature of the population distribution (normal, uniform, etc).  Thus, a nonparametric test would be required.  I know of no nonparametric tests for any of the statistics you have (minimum, maximum or range).\n\nIf it is possible to obtain the actual values for each of the samples in each set, the Kruskal-Wallis test should suffice.  It is nonparametric, so there are no problems with the requirement of assumptions about population distributions.\n\nFurther, its job is to determine if two samples are far enough apart such that the null hypothesis that the population distributions are the same can be rejected.  This would give you what you desire.\n\nAs far as your problem is concerned, the only statistics you have are minimum, maximum and range.   Perhaps one could derive a test to test the minima or maxima of each set, but one still runs into the problem that there can be no parameter placed on the population distribution.\n\nIf you can make an assumption about the distributions of the populations of interest (normal or whatnot), then it would be possible to derive such a test.  This would be a tedious task, however, as it would require thorough knowledge of order statistics and distribution functions.\n\nOne would need to use a test statistic based of off the difference in the two statistics that you use (I would use the maxima since they are further apart than the minima and probability distributions for ranges are uncharted territory unless you're a Ph.D.).  Thus, your test statistic is based off of the difference between the maxima (Y(n) - Z(n) where the (n) are subscripts, which is 2 in this case).\n\nTo perform this test, you would need to derive the probability distribution of the difference of the two maxima and use that to find the probability of the difference being two or greater.  This is quite a task for anyone and requires a large amount of knowledge in mathematical statistics.  I believe I could perform such a task, but it would likely take some time to complete.\n\nRemember though that you must make a critical assumption about the population before you can even do this.  I would recommend using the assumption of uniformity if you are unsure of the populations' distributions.  This is because the derived test would then be least likely to give a type I error, where you reject the data improperly, because the test is less likely to reject.
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you do not need to have an r^2 value to determin if two values are correlated\n\nyou just look at the sctter plot\n\nif there is a a clear trend (ie as I increase one value the other value generally decrease  or genrally decrease)\nthis can be seen in a simple scatter plot one need no statistics to to see if there is correlation\n\nif ther eis no correlation the scatterplot will be totally random and no  pattern. Correlation you are generelaly looking for a linear pattern)\n\nif you wnat to technically test and  to have a significane value you can test Ho: R^2= zero  vs   H1: R^2 ~= zero\n\nthe problem you will encounter her is when you calculate R^2 you will only be able to get a point estimate. \n\nIf you wish to test the value of R^2 equal to zero then you will need to somehow to guage the variance.\n\nTo guage the variance of R^2 you will have to utilize the jacknife variance estimator or the bootstrap variance estimator\n\nthes are methods to compute variances for thing besides the mean ie the median, the max , the min, R^2\n\n\nto jacknife you compute the R^2 n times always leaving the nth observation out of the calculation. this gives you a range of R^2's. now you can compute a variance to test or use that set to define a CI to do your test.\n\nThe bootstrap is similar in that you compute the same value multiple times, but here you will take random samples with replacement from your samples until you have chossen n observations (not with replace ment so you get a differnt sample than you started with) now compute the value of intereest (R^2) off of this sample. this will give you another estimate for the same value. Now reapeat this exercise until you have a sufficent number of estimatess (say n) then we can now compute a variance for R^2 or use the set to define a 95% CI to do our test jsut as with the jacknife method
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I beleive that you can use the xmp tag, the only downside is that it uses monspaced font and puts it on a new line.\n\nYou can also use a textarea to display it.\n\nThe best solution, though, is to use unicode characters for the triangular brackets.  To use this, just use an ampersand (&) folowed by lt; for the first bracket and an ampersand (&) folowed by gt; for the second bracket.\n\n---\n\nHope this helps!\n\n(edit - I so cannot spell today...)
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If you are talking about z scores for one particular variable, then you should use a histogram.  Excel does not do histograms, so you should probably use MINITAB or some other statistics program.\n\nThe previous answer only applies if there are two variables involved.
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We can use graphs to create a more meaningful and visual representation of data.  For instance, pie charts represent the percentage of a data set (51% white, 49% black).  Line graphs can show trends (the rising sales of hybrid vehicles vs. the falling sales of gas-guzzling vehicles).
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The width of the box (more technically called the interquartile range) is used to define the maximum length of the lines (whiskers) that come out from the box.  That max length is 1.5 times the box width.  Anything beyond that is deemed to be an outlier, and shown as an individual point.\n\nThe wikipedia writeup on boxplots has a nice graphical example, I suggest you try the link below.  It shows both "mild" and "extreme" outliers.
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The Trend Analysis module allows you to plot aggregated response data over time. This is especially valuable, if you are conducting a long running survey and would like to measure differences in perception and responses over time.\n\nThe following data points can be measured (Y-Axis)\n\n   1. Mean and Mean Percentile\n   2. Standard Deviation and Variance\n\nThe "Time Factor" (X-Axis) can have the following granularity\n\n   1. Daily\n   2. Weekly\n   3. Monthly\n   4. Quarterly (Jan-Mar, Apr-Jun, Jul-Sept, Oct-Dec)\n   5. Yearly
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Used to set insurance premiums, the probability of getting into accidents based on your car type/age\n\nFor evaluating worker performance, ie you're on time 95% of the time or only 80%\n\nHow can you actually tell when two sets of data are different? Need statistics.\n\n A lot of jobs in stats!!
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I was answering another similar question, and I came across a website that has a downloadable sample spreadsheet for doing statistical distributions.  Play with this and see if it helps you out.
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I find it hard to believe that the pairs are treated independently. There's no point to that, it doesn't give you any useful statistical information.\n\nHowever, if you're trying to compare all the values on the left side as a group with their pairs on the right side as a group, then you could use a modified version of the Chi^2 test. Basically multiply everything by 100, and then do a normal Chi^2 test.\n\nYour degrees of freedom will be (6-1)(2-1) = 5
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It is like what the other answerer said\nI know it is the offset of a value of the average of that list\nin Excel use:\n=STDEV( A1:A4)\n\nVBAXLMan
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http://www.utexas.edu/its/rc/tutorials/stat/spss/spss1/
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Are you sure that "somm'd" is correct?  Check  the spelling please.\n\nI don't know what gamma and lambda are.  Are they test statistics?\n\nWhat do you need to know about scatter plots?  They are just x-y plots of data points.
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If you can provide more details then I can help you. p-value has to be compared with the level of significance. z-table and t-tables are different and u'll requir specific table for that. are you solving a question of hypothesis testing??\n\nCritical value for 't' requires the degree of freedom.
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X=[1,2,3,4,5];\nY=[4,10.72,17.42,37,55.7];\n\np=polyfit(X,Y,2);\n\nxPlot=0:.1:6;\nyPlot=polyval(p,xPlot);\n\nplot(X,Y,'o',xPlot,yPlot)\naxis([ 0 6 -inf inf])\nlegend('Data','Second order fit')