Can someone explain a 'moving average' to me, with examples, please.?
144787
I will give you just the simplest possible explanation to get you started. It can be far more involved than this, however. \n\nThe Simple Arithmetic of the Moving Average\n- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\nSuppose you have a string of numbers: { 0 3 1 2 5 3 2 }\n\nIf you suspect that this string of numbers contains some sort of signal, but it is hidden or obscured by noise, you can use a moving average to try to find it. \n\nFor example, suppose that at a time t, you measure a number x, to get x(t): \n\nx(1)=0 \nx(2)=3 \nx(3)=1 \nx(4)=2 \nx(5)=5 \nx(6)=3 \nx(7)=2\n\nThe t values could be seconds, for example, so t=1 is the first second, t=2 is the 2nd second, t=3 is the 3rd second, etc. Then:\n\nx(1) is the x measurement at the first second\n\nx(2) is the x measurement at the second second (that sounds funny doesn't it? )\n\nx(3) is the x measurement at the third second \n\nand so on.\n\nOne could form a moving average of these numbers, to try to find the underlying signal. Let's call the moving average y(t).\n\nLet's suppose that we will average 3 numbers x to find the number y:\n\ny(2)= (1/3)*[x(1)+x(2)+x(3)]\ny(3)= (1/3)*[x(2)+x(3)+x(4)]\ny(4)= (1/3)*[x(3)+x(4)+x(5)]\ny(5)= (1/3)*[x(4)+x(5)+x(6)]\ny(6)= (1/3)*[x(5)+x(6)+x(7)]\n\nSo you see what is happening? The three x values to be averaged are chosen to be x values in a group, and the x values chosen are shifted as the argument of y moves. The values to be averaged move as the argument of y moves,  \nso y(u) = [x(u-1)+x(u)+x(u+1)]/3. You can get fancier, and invent a method to find y(1) and y(7), although there is no x(0) or x(8) to use in the formula. \n\nExamples:\n- - - - - - - - - - - - - - - - -\n\nHow might one use this in practice? Suppose that you want to see the effect of your new traffic signal on traffic flow on the highway. You put a meter down and count how many cars pass per minute, and collect data for 10 days. You want to analyze this data and see if you can see a pattern or a trend in your data, and compare it to data from before you put in the traffic signal. Only problem is, the data are very noisy so it is hard to see a pattern. The solution? Try a moving average to smooth out the lumps in the data to see if a nice pattern emerges that you can compare with the pattern from before you put in the traffic signal. \n\nAnother example is, you are trying to listen to your friend's voice over a microphone on his computer, but there is really so much static that you can hardly hear him. Then, you could use a moving average to try to smooth out some of the noise so you could hear his voice more clearly. \n\n\nMore Advanced:\n--------------------------\n\nOne could make the number of values of x to be averaged larger. One does not have to use an arithmetic average, so another kind of average could be used, like a geometric avearge, or a harmonic average to form y. One does not have to use averages at all, so a median or a mode could be used to find the central value of the x's in the group being analyzed. This is a very simple type of low pass filter, or a procedure to remove high frequency noise and other signals from sequential data. The moving average can be computed using a mathematical technique called convolution. There is a connection between convolutions and an expansion of these measurements in terms of sine waves (called a Fourier transform) that is very interesting and useful. \n\nIf you are interested, there is a huge amount of information about this on the internet. I did not want to overwhelm you to start, however. It is best to start out really simple and understand the simple case very very well before you move on to more advanced material.
1130968
Everything seems to be used for analysis, to find the mean curve average...TV....Internet.....I beleive you may be right.\n\nBut hey...Cheer up...Humans just aren't that average. Every time they think that they have found the perfect way to market, advertise and co-erce you into buying a chocolate bar, some other spotty face nerd in the future revolutionises the process.\n\nAll of this analysis is just a sea of change and in a hundred years...BAH!...Who's gonna' care.
261755
Keep in mind that what you're doing when you add the logs is multiplying the ratios.  Then dividing by 3 takes the cube root of that.  So averaging the values is the arithmetic average and averaging the logs is the geometric average.  Which one is fits what you're doing best?  You'll have to figure that out.  I'd guess the geometric average based on the formula you've given.
820876
http://finance.yahoo.com/q/bc?s=GBPUSD=X&t=2y
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.
242624
http://en.wikipedia.org/wiki/Mean_%28statistics%29\n\nThis is a link from Wikipedia. It shows lots of examples along with explanations.
565402
That's funny \n\naverage average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average hahahahahahahahahahahahahahahahahahahahahahahahahahahahahhahahahahaha!
857275
In a weighted moving average (WMA) the most recent periods have the most influence.\n\nIf x[1] is the data for the most recent period, x[2] for the previous period, ... , x[n] for the oldest period then\n\nWMA = \n\n( n x[1] + (n-1) x[2] + ... + 2 x[n-1] + x[n] )\n- - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n               n + (n-1) + ... + 2 + 1\n\nI assume that 16 is the data for the (oldest) first month, 26 for the second month, and 21 for the third (most recent) month.\n\nWMA = ( 3 × 21 + 2 × 26 + 1 × 16 ) / ( 3 + 2 + 1 )\n= ( 63 + 52 + 16 ) / 6 =  131 / 6 = 21.83
951978
Look up convergence theory on www.colorado.edu/communication/met.  It will explain this theory in fairly simple terms.
584250
In statistics:\n\nmu = average = (sum of all values) / (total # of values)
771697
Try Google or Yahoo.  You'll find worked examples there.  I think it needs diagrams to understand; they and the accompanying explanations are too complex to attempt to reproduce here.
352702
There are a number of methods.  To help you locate (i.e. a Google Search) which method is best for you, consider the following specific methods:\n\nARIMA\nLinear/Non-Linear/Log Linear Trending\nFirst Differencing\nWinters\n\nStep 1:  Plot your data ... always plot your data\nStep 2:  Is there a Seasonal Component ?  Example:  If it's car sales, well then probably ... if it's pounds of beef purchased each month, probably no.\nStep 3:  Is it linear or could it be made linear (i.e. if you plotted the natural log of the values, does that straighten out the pattern a bit ?\n\nTime series forecasting can be very interesting ... I suggest that you seek out someone with a little experience in this area that can help you out.  This is generally college level (first/second year undergrad) stuff, so it's not too difficult, but not something that you'd really want to just "learn on your own"\n\nI do this professionally and would be glad to help (don't worry, I won't charge you).  Step 1 for that would be to send me your data and we'll take a look at it.  My contact info is in my profile.
259218
ARIMA combines autoregression--which fits the current data point to a linear function (usually) of some prior data points--and moving averages--adding together several consecutive data points and getting their mean, and then using that to compute estimations of the next value.\n\nThe advantage is that, with enough elements regressed and averaged, you can fit an approximation to almost any time series you like, to whatever precision you like.\n\nThe trouble, of course, is Slutzsky's theorem:  Slutzsky showed that, by using ARIMA type computation, and perhaps adding a trend line or two, you can take random noise into any time series you like...  The point?  Well, it basically means that you may fit the data magnificently, but the ARIMA fit could still be total nonsense...
981618
I guess 1) just wants you to write: Want dA/dt, have da/dt and db/dt.\n(From now on I'll use A' to mean dA/dt etc).\n2)\nA=π*a*b, so using the product rule, A' = πa'b + πab'. So A' = 7πb - 11πa.\n3) Just substitute in: A' = 7*π*50 - 11*π*30 = 20π, so thats the rate it is increasing.\n4) Again substitute in, A' = 7*π*20 - 11*π*40 = -300π, so now its decreasing at that rate.
562252
Nobody knows which direction the market will move in the short term.  Indicators that have any value will immediately move the market, since market prices are based on expectations of the future.
434195
That is the rate of change in an "instant" in time.  You calculate it by finding the slope of the curve at that point.  You do this by taking the first derivative at that point.  If it is a straight line, you can get it by doing the average rise over the average run:  Pick two points on the line. Call them x1,y1 and x2,y2.  You are probably looking at supply and demand so we'll do price and quantity, so call them Q1,P1 for the first point and Q2,P2 for the second.  The slope (instantaneous rate of change) is: (P2-P1)/(Q2-Q1).\nIf it's not a straight line, you can approximate it by picking one point to the left and one point to the right of the point you are trying to find.  Make these two points close to the point you are looking for, and equidistant from the point you are looking for.  Use these points in the above equation.
323008
By calculationg it's mean. Add all the values of observations and then divide by the number of observations.\nThat's all!