What is the main difference betweencovariance and correlation?
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-- CORRELATION --\n\nA measure of the degree to which changes in two variables are related.\n\nThe standard measure of correlation is the correlation coefficient, a number between minus one and plus one that indicates the strength and direction of a linear relationship between two variables.\n\nA correlation coefficient of minus one indicates that they are perfectly negatively correlated, zero that they are not correlated at all and one that they are perfectly correlated.\n\nCorrelation risk is the risk that two variables or instruments are unfavourably correlated.\n\n- - COVARIANCE - -\n\nA measure of how two random variables behave in relation to each other.\n\nMatrices of covariances are used in several different models.\n\nIt differs from correlation in that it incorporates measurements of the magnitude of the variations as opposed to the correlation coefficient which is dimensionless.\n\nThe correlation coefficient between two random variables is equal to the covariance between them divided by the product of their standard deviations.
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"Descriptive statistics" are typically distinguished from inferential statistics. With descriptive statistics you are simply describing what is or what the data shows. With "inferential statistics", you are trying to reach conclusions that extend beyond the immediate data alone.\n\nScience deals with the natural world. Technology is the study of the natural laws which govern the universe.
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the independant variable is the one that you can change/control, e.g. time\nThe dependant variable is the one that changes outside of your control. e.g. distance travelled. \nOn a graph you plot the independant variable on the x-axis and the dependant on the y-axis
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A variogram is the basic tool in geostatistics for describing spatial correlation. Autovariogram is used to describe autocorrelation of a single continuous variable or a single class, and cross-variogram is used to describe cross-correlation between continuous veriables or classes.
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I don't know enough about your specific topic but you can express variables in science as ratios or functions of other variables.\nIn order to be specific about how these variables effect and relate to each other you must then be specific about the values of the invariables, or constants as they are more commonly called.\n\nSo .. you should be able to relate and analyse variants in terms of the invariants but without being specific as to the nature of the invariants.\n\nI think that would work.
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The key point is that, since the two normal vbls x1 and x2 are independent, their difference \n\ny = x1 - x2 \n\nis also normally distributed with mean \n\nm = m1 - m2 \n\nand variance \n\ns^2 = (s1)^2 + (s2)^2\n\nor std dev \n\ns = sqrt[(s1)^2 + (s2)^2]. \n\n(Note that its the variances that add up, *not* the std devs.) The probability that x1 > x2 is the prob that y > 0, and we know the distr of y, so we're nearly there.\n\nTo use tables or a computer prog, standardize y to \n\nz = (y-m)/s = [y-(m1-m2)] / sqrt[(s1)^2 + (s2)^2]\n\nwhich has zero mean and unit std dev. What we want is the prob that z > -m/s which is \n\n1 - Phi(-m/s) \n\nwhere Phi is the std normal c.d.f. \n\nBut computer progs usually give you not Phi but the error fctn \n\nerf(x) = (2/sqrt(pi)) integral from 0 to x exp(-t^2)dt\n\nwhich carries the same info and gives you Phi as \n\nPhi(x) = (1/2)(1 + erf(x/sqrt(2)).\n\nSo you need to put into this \n\nx = -m/s = -(m1-m2)] / sqrt[(s1)^2 + (s2)^2]\n\nand then subtract from 1 (because what you need is 1 - Phi).
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You can calculate Pearson's r for any two variables, including number of inadequate questions vs stupid answers, my weight vs my points in Y!Answers, and anything that you feel like. \nJust remember that correlation does not imply causation.
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Beta is a measure of the market's risk measured by randomness of data. Correlation is whether stocks lose or gain value with each other. Similarity is that a risky stock is more likely to be uncorrelated with the market.
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A variable is something that changes.\n\nA control is the thing in your experiment that helps show that tehre are no flaws in it.\n\nCurrently, I'm doing an experiment on the effect of color on a child's brain to follow directions. In this experiment, I give 40 children the same exact test buttt the sheet of paper's color whcih it is printed on changes.\n\nThe variable of this experiment is the different colored pieces of paper\n\nThe control would be the classroom I test it in (do the experiment in the same class each time) and the WHITE piece of paper which will show that there IS an effect.
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The first post is copied directly from Wikipedia. How sad really.\n\nHere's the link for you\n\nhttp://en.wikipedia.org/wiki/Normal_distribution\n\nAnyway, continuous distribution is exactly the same as normal distribution, as you mentioned. It's Just another way to say it really. I also think that it is a bell-curve without variance.
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Any equation explaning the relationship between two or more observations (or data points) could be called a regression. It's really an extrapolation of the underlying line (or non-linear relationship) that explains the variance.\n\nPerhaps you could do a simple one- pick a stock and use the closing price over a period (say 3 weeks, 15 observations). Then try to come up with a formula to explain the pattern.\n\nActually, that one is kind of hard without stats software. But there are others- such as average temperatures in a given area...
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confounding variables are outside things that can effect results. example: examining the relationship between fish growth and food, salinity, and temperature all in the same tank. can't tell what's making the fish grow, can ya?\n\nexperiments reduce variability by controlling the environment more thoroughly.\ntake a statistics class, that helps with bio
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If both the dependent variable (Y) and the independent variable (X) are standardized, then both their means are zero and standard deviations are one.  The correlation coefficient is not effected by a change in scale, meaning that the correlation doesn't change if you add or subtract the same number from each X or Y or if you multiply X or Y by the same positive number.  An example of this is if the correlation between height and weight when height is in inches and weight is in pounds is 0.6, then the correlation if height is in centimeters and weight is in kilograms is also 0.6.  We would hope that the relationship between the two variables doesn't change if there is a change in how the variables were measured.\n\nThis is the case when standardizing, since standardizing has you subtract the mean from each value and then divide by the standard deviation.  So the correlation coefficient for the standardized values of X and Y are the same as the correlation coefficient for the original values of X and Y.\n\nNow, the formula for the slope coefficient is r(sy)/(sx), where r is the correlation coefficient and sx and sy are the standard deviations of X and Y respectively.  Since both X and Y are standardized, then both of their standard deviations are equal to one, so putting that back into the expression above, the slope coefficient is equal to r.\n\nTo summarize, if X and Y are standardized, then the slope coefficient is the same as the correlation coefficient.
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If you give only obscure details (B1, T/F) but tell nothing more about the problem, you are not likely to get an answer....I do a lot of statistical correlations, but this really doesn't mean much.\n\nNow, my guess on what you mean.\nYou have a series of data points (x_n, y_n). If you make a scatterplot and the points are rougly on a line you have perfect correlation, correlation coefficient is +1 or -1.\n\nIf on the other hand Y doesn't depend on X, i.e. correlation coefficient 0, then when you do a linear regression the slope of the regression line will be close to zero, as the data points (x_n, y_n) are scattered randomly.
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A relatively low cost, easy to operate video signature comparator and a corresponding method by which a known signature is visually compared to a sample signature to determine whether both signatures were made by the same individual. The signator comparator has particular application to forgery detection or to applications for verifying the authenticity of a signature. An electronic circuit is disclosed which displays video signals that are representative of the known and sample signatures. The circuit enhances video image density variations through extreme contrast and brightness adjustment and forces the lighter regions of each signature to white, such that only the dark stroke lines (indicative of those regions having the greatest density where the maker applied the most pressure) are displayed on video monitor. By visually comparing the characteristic density/pressure variations of the known signature to the sample, an observer can more accurately determine whether both signatures originated from the same maker.
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The roots of this problem lie in the fundamental problem addressed by kalman filters. You want to read up about kalman filters to fully understand the solution.\n\nHere is the approach...\n\ngiven two estimates of a value that is normally distributed, you assume that a better estimate exists with value E = wE1 + (1-w)E2.  The variance of this new estimate is given by S^2  = w^2.S1^2 + (1-w)^2.S2^2.  You need to differentiate the second equation, look for a minima and plug that value of the weight into the first equation to get your optimal estimate.  Hope this helps.
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Correlation is determining the relationship of one variable to the values of other variables using collected data.\n\nFor example if you considered a person's salary, you might find it correlates with: number of years of education, and number of years of employment, along with other variables.\n\nCross-correlation means a correlation just between 2 variables. You get that expression, because you can evaluate if two variables cross correlate, by plotting one variable on the X and one on the Y axis of a graph. If the cloud of points that result are clustered around a straight line then they cross correlate well.