Can someone help me out with setting up a statistical model for my analysis?
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If it were just two independent and one dependent it would be classic ANOVA.  MANOVA is traditionally used for multiple dependent variables.  Imagine that you were ran the ANOVA for the entire sample, then run separate ANOVAs for each of the following:  above age above edu, above age below edu, below age above edu, and below age below edu.  In the separate runs the data would not be symmetrical (unless the age and edu distributions are completely homogenous) so comparing the relationships would be useless.  The same applies to if you ran an ANOVA for above age all edu, because if age and edu were at all correlated, the mean edu would differ from the full sample and you couldn't compare the two.  In SAS there are corrections for skewed data which you could feasibly use.  MANOVA is probably an option too, if you use some sort of repeated measures MANOVA (where one set of independents measures the dependent variable is the other set is age and edu (dichotomous) and they measure the dependent variables of the relationships of the first study) .
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ok. i think i know what you're asking.\n\nDo you have a chart from whatever you're doing filled with information? Put that in your Results and Analysis section.\n\nBasically, just put anything with numbers into that section.\n\nYou could try to be just a little more vague when asking your question though  ;)
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You would look for a company that does data analysis or data mining. http://metricconsultants.com/ usually it would be some sort of consulting type of firm such as this one.
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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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I sent you an email to madkins007(at)yahoo.com \n\nIn it, I put a basic spreadsheet that should be a good start for you.
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use the formula for confidence interval.. its found on most statistics book.. i would type the formula, but that wouldn't be helping you to be a better student..
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may http://www.nicetermpaper.com help\n\nyou can also check with Prof. Mike @ info@thesisspecialist.com
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No problem \n\ngo to google and search under "Z table" +statistics. \n\nYou'll find a lot.
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A Excel spread sheet can be setup for this....so get some books on "Excel" and have a go or you could ask someone to make one for you.
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This looks ok:\n\nhttp://www.wadsworth.com/psychology_d/templates/student_resources/workshops/stats_wrk.html\n\nor else just keep google-ing psychology+statistics.\n\nGood luck :-)
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Hi! I'm a statistician and I use http://www.statsoft.com as well as cheap textbooks from Half Priced Books in my area. Both seem to help. If you have a specific question, feel free to email me. Don't know how much good I'll be because I use fancy software for my analysis, but I'll try.
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find a better Statstic book, or use CHIDIST / CHIINV functions in MS-Excel. Or use this thing:\nhttp://www.georgetown.edu/faculty/ballc/webtools/web_chi.html\njust make sure it gives same numbers for lower df's as your table
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Here is a link to a users manual-specifically the statistics section. You can go to the full contents.\nhttp://www.infj.ulst.ac.uk/NI-Maths/Guides/TexTI-85/Ch3/THBStats.html
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Excel is a great tool.... I'm sorry I don't remember all the specifics but I know that there is more than one way to do it.  If you can narrow down which variables you expect to be most related that would be a great start.  If you don't have a physical (real-life) basis for that, you can try graphing all 6 variables against a column just numbered with integers from 1 to n (the number of data in each column).  Then you can plot pairs of columns against each other and find equations to fit the curves (polynomial up to 6th order, exponential, logarithmic) and use the R squared values to determine how well the equation represents the correlation of the data.  (R squared = 1 is a perfect match.)  Now when you start getting into cases where there are multiple independent variables effecting the dependent variables, it gets a little more complicated, and unfortunately I don't have Excel on this computer and I don't have my old notes or textbooks with me.  I hope this at least helps you get started.
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http://en.wikipedia.org/wiki/Econometrics\nPlease click the above link to know in detail.
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Standard plot of what?  What is the analysis parameter you are interested in measuring?
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Statics?  Statistics?