choice of parametric model in survival data analysis?
599454
Survival of what exactly?\n\nYou could try an exponential  N=A*exp(-Bt) as used for radioactive decays, with N the nu,ber of survivors.\n\nA logistic function if the numbers of deaths show a normal distribution in time, like for car spare parts.\n\nhttp://en.wikipedia.org/wiki/Logistic_distribution
202023
This is a broad topic with a lot of variables depending on the particular nature of the source of the problem, your age, how much damage is present currently, as well as some presenting characteristics of your disorder.  So any average number might not be too helpful to you as YOUR condition may not be average at all.  \n\n"Estimation of mortality rates is affected by the same factors that make estimation of incidence difficult. In the series of 99 patients discussed above, the probability of surviving 24, 48, or 60 months was 83%, 72%, and 64%, respectively. Mean survival interval from onset in this group of patients was 47 months. Factors associated with poor outcome were the presence of pulmonary hypertension at the time of diagnosis and a final diagnosis of DIP or pulmonary vascular disease. Other smaller series also suggest that patients with DIP have a very poor prognosis, particularly infants. This is in marked contrast to adult ILD, in which DIP diagnosed histologically indicates steroid responsiveness and a better prognosis."  Quotation from the second reference.\n\nCheck out the references below for more information.  Print them out and discuss the parts that are not so clear, with your physician.  Most doctors are pleased that their patient is becoming well informed on their illness and will be happy to help.  Good luck.
17841
You might try DAP. You can find it here along with a lot of other free statistical software.\nhttp://members.aol.com/johnp71/javasta2.html
319924
It depends on the distribution.  Some probability distributions have multiple parameters (such as the mean and standard deviation for the Normal distribution).  Phi2 would be one parameter of the particular distribution used in the problem.
222877
There are some fields where accurate measurements are not possible.In such cases statistical tools are very useful;eg- significance testing & hypothesis could be very useful where the size of population is either very large or unknown.For this purpous various formulas have been derived.\nhence the statistical tools can be used to analyse situations like change in population after 10 years,degree of defects in blubs manufactured etc.
100009
I am not quite sure what the question is. What should the manufacturer do? Did they already do the experiments or do you want to know what sort of experiments to do? Do you want to know how to reduce the data? How to present the data? What statistical tools might be useful?
43772
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.
883446
Hi, it depends on particular organisms. Check the link below for basic mathematical formulas you may be able to use.\n\nA study is done in which the number of bacteria in a liquid culture is monitored over a period of several hours. Ten observations were made:\n\n\nTwo questions need to be answered:\n\nIs this data consistent with an exponential growth model: N = N0ekt, where \nN0 = the number of bacteria present initially (at t = 0)\nk = a growth rate constant\nN = the number of bacteria present at a given time t,\nand e is the fundamental mathematical constant forming the base for natural logarithms. \nIf the answer to the first question is "yes", then what are the values of N0, k, and t2 (the so-called doubling time) for this bacterial system?\nThe experimental measurements listed above indicate the number of bacteria are growing with time. If we plot the observed values of N (vertically) against the elapsed time, t, in hours, we get a graph (available on web page below).\n\nYou can draw a smooth curve through the experimental points as shown in this figure, and the points all seem to lie relatively close to this curve. Further, this curve looks quite a bit like the graph of an exponential function, but from what we see, it might just as well be part of a parabola or of one of a number of other curves that result when various algebraic forms are graphed. This graph suggests that the answer to the first question may be "yes", but we would usually want stronger evidence than this to draw a definite conclusion. The trouble here is that it is difficult to distinguish precisely and reliably between the curves are the graphs of quite different mathematical functions.\n\nThere is, of course, one curve that is easily recognized by everyone - the straight line! In this situation, the logarithmic function can be used to rewrite the exponential growth formula into a form that will allow us to create a plot that does give a straight line when data is the result of an exponential growth (or decay) process. Start with the exponential growth formula:\n\nN = N0 ekt\n\nSince this involves an exponential with base e, it is "natural" to use natural logarithms - those based on the number e. Using the more common notation ‘ln’ for ‘loge’, we simply take the logarithm of both sides of the previous equation:\n\nln N = ln(N0 ekt)\n\n= ln N0 + ln(ekt)\n\n= ln N0 + kt ln e\n\n= ln N0 + kt\n\nHere, we have used two of the three basic properties of logarithms to manipulate the right-hand side, namely that\n\nlog (AB) = log A + log B \n\nlog (Am) = m log A \n\nFurther, in the last step we took advantage of the fact that by choosing to work with natural logarithms, ln e = logee = 1. Thus, the exponential growth formula can be written in the logarithmic form\n\nln N = kt + ln N0\n\nIf the exponential growth formula is correct and we were to plot ln N along the vertical axis of our graph (rather than N itself), and t along the horizontal axis, then the equation of the plot we are preparing will have the generic form\n\nvertical quantity = constant x horizontal quantity + another constant \n\nBut this is just the equation of a straight line. The plot that results using the data given above is available on web site below.\n\nAlthough the values written on the vertical axis are values of N, this graph is ruled vertically so that the vertical distances are actually proportional to ln N. Since one scale is logarithmic (the vertical one) whereas the other graph scale, the horizontal one, is the ordinary uniform spacing type, this is called a ‘semilogarithmic plot’.\n\nAt any rate, the figure indicates that the points are scattered about a straight line, the scatter due to small random errors in measurement. Since the plot of ln N vs t gives a straight line, we have confirmed that\n\nln N = kt + ln N0 (equation *) \n\nis a valid formula for this data, and therefore, so is its equivalent, N = N0 ekt, valid for this data.
623677
Analysis of variance or ANOVA checks for the variation contributed by teh population in question
956580
In Excel you can graph the data and then add a trendline.  You can even have it display the equation for the trendline.  Some possible choices for fit are linear (line), exponential, polynomial of degree 2, 3, 4, 5, 6, etc.\n\nIf you give some sample data, I might be able to help further...
1030781
By reading your question, it appears that you are first and foremost seeking to "define" mathmatically what it is that you are attempting to do to reach your results-- presumably so that you can study that area of statistics further and apply it to your experiment.\n\nWith that in mind, what you are dealing with in your experiment is called "Event Probability."  In Event Probability, you are dealing with concepts of "conditional probability", "frequency", and "relative frequency."  For instance, what you are doing is very similar to the flow charts that appear in many family's medical books with a "Decision Tree" that allows you to answer "Yes" and "No" questions along a Flow Chart to reach an ultimate probability that you have a particular illness.  In other words, you are creating an algorithm by measuring the "relative frequency" of events and compound events and drawing a conclusion from that information.  People who perform these experiments are called "frequentists."  For all intents and purposes, this is what actuaries do at insurance companies and financial firms.  I'd read about those areas of statistics first which should prove very helpful in providing you with a structure for reaching the most helpful and accurate conclusions from your experiment.
308
First load the add-in:\nTools > Add-Ins > Analysis Tool Pack\nthen find it:\nTools > Data Analysis & Choose Regression\nIf you need any more help mail me.
420718
ANCOVA is a regression/ANOVA technique. as a result, it assumes a interval level (or higher) response variable. It also assumes a normal error term to the model, and random and independent observations. Failure to meet these conditions by the data, would make such techniques inappropriate. MANCOVA, like all multivariate techniques, requires all the above, plus sufficiently large sample sizes to accurately estimate a variance for all cells (at least 3 per, but much more is helpful to reduce Type II error), for each and every response variable. Outside of those stipulations, you can use ANCOVA/MANCOVA as the application demands. ANCOVA is meant to be used as a blocking criteria, either for correctly handling dummy-coded categorical response data in a multiple regression analysis, or handling continuous data in an ANOVA setting. An example of the former would be, say, an examination of pay vs. age, blocked on gender. An example of the later would be for introducing pre-test scores as a continuous variable into an ANOVA of post-test score versus treatment groups and other criteria. Without more information on the application, that's all I can tell you.
181649
what statistical informations can be extracted from difference between actual and estimate data?and how?
356040
elementary mode analysis Is also known as extreem path way analysis. It defines the limiting bondaries of the operation of a metabolic network. The normal approach is to use singular value decomposition to determine the constraints of a metabolic network.\n\nTo be honest its a bit over rated since all it does is define the feasable space of operation of a cell. You have to use other principles such as cyberbnetics (maximise growth rate and then use linear programming) or kinetic modeling to determine the expected operation of a cell. \n\nTry a search for Palson - but dont cite or talk to him because he is an arrogant self promoting w****r who doesnt cite competing groups work.
276280
yes but it doesnt do a real good job of it. you need the data analysis tool pack and then just run a regression and see if it works out for you. i use eviews for mine but there are a ton of other regression analysis tools to use. the only difference is the interface and the amount of the commands that you have to type in.
624712
This problem is solvable.\nProbability of event A is written P(A).\n\nP(A)= Total # of outcomes that correspond to event A (divided by -or- over) Total # of outcomes in sample\n\nAnswer:\nP(A) = 12/20 = 3/5