what are the features/characteristics of a positive correlation graph?
492336
As X increases, Y also increases.
922060
My suspicion upon seeing this question is that it can be solved using mathematical induction.  After all, couldn't we simply 'build' such a (maximally connected) graph starting with n=2 vertices and 'grow' it from there?\n\nRather than take on the proof myself -- mostly because I am unsure of the definition of 'components' -- I leave it as an exercise for you.  However, it bears noting that the formula shown is the commonly accepted "sum of the first n integers" formula used in almost every text on induction.\n\nGood luck!
1003267
You should look at the hamming distance between the two binary vectors, which is defined to be the number of coordinates that  have different values.  So in your example:\n  0001111000001100\n  0011110000001000\nhave 3 coordinates that have different values and\n  0011100000000000\n  0000000001111111\n10 coordinates that have different values. \n\nIf you define the correlation to be the number of coordinates in total minus the coordinates that have different values, you'll have a high correlation for the first pair, and a low correlation for the second pair.
113879
Bar graphs for things that are either/or, e.g. brown eyes, blue eyes, green, gray, etc.\nPie charts (graphs) to show proportions of the whole, e.g. percentages.\nHistograms (look like bar graphs) for classes of items, e.g. heights of people, say 60-65 inches, 66-70 inches, etc.\nLine graphs for continuously variable data, e.g. speed of reaction against temperature. These graphs have independent and dependent variables, and may be straight lines or curves. Very good for mathematical interpretation.
883821
There are two types of variables on a graph - independent and dependent.\n\nindependent variables are on the "X" axis.  They are the variables that you change during the experiment.\n\ndependent variables are on the "Y" axis.  They are the ones that change due to your manipulation of the independent variables.
72398
I found a nice and simple online grapher at http://www.walterzorn.com/grapher/grapher_e.htm
36510
A graph is an illustration of the relationship between two properties of the same data points. It is also possible to have three or more properties shown on one graph for the same data. Examples would be a graph of temperature by time of day. Another graph might show temperature on one axis, date of the month on another axis, and could have rainfall on another axis. Spider graphs allow graphing many different properties on one graph. Three dimensional graphs with x,y,and z axes can also do this. \n\nTables are simply an organized list of the values of data, arranged in columns and rows, where the rows can be for unique data points, and the columns are for the properties of those data points, or vice versa. Graphs are generally used to demonstrate visually the relationships of the data in the table.
353110
This sounds like an excellent application of random graph theory (each person is a random vertex, and each edge has a weight giving each path a weight as well). I read a few paragraphs on this a while back, but its been too long to fashion an application for you. I think you will be interested in it.\nCheck out Random Graphs, by Bollobas or The Strange Logic of Random Graphs, by Spencer.
1094481
A single graph could have many possible applications.
249872
a. having both positive and negative end
727463
A graph is a way to visually show the relationship of different numerical values - there are lots of different types of graphs:  Pie, line, area, bar, etc.\n\nCheck out this site:\n\nhttp://nces.ed.gov/nceskids/createagraph/
838358
i can't give you the answer but i used my ti-83 and i can tell you the points of the graph...\n(1,0) (2,4) (3,8) (4,0)\n\nHope this helps
450656
The statement is true.  The formula for the slope of the regression line is\n\nr(SDy)/SDx\n\nSince SDy and SDx are always positive, then the only thing that determines the sign of the slope is r, the correlation coefficient.  So if r is positive, then the slope is positive; if r is negative, then the slope is negative.
712712
[on] [y=] [(] [x,t,o,n] [+] [2] [)] [/] [x,t,o,n] [enter] [5] [-] [2] [x,t,o,n] [enter] [(-)] [2] [x,t,o,n] [-] [3] [enter] [window] [(-)] [5] [enter] [5] [enter] [1] [enter] [(-)] [5] [enter] [5] [enter] [graph]\n\nYou should have on screen a vertical asymptote at x=0, horizontal asymptote at y=1, a curve going to negative infinity towards 0 on the left, a curve going to positive infinity towards 0 on the right, on the left tangent line y=-2x-3 touches point(-1,-1), and on the right tangent line y=5-2x touches point(1,3).
743863
The most common correlation (there are several) is described at:\n\nhttp://mathworld.wolfram.com/CorrelationCoefficient.html\n\nThis is sometimes known as the "product moment correlation".  Some others are: Spearman's rank order correlation and Kendall's tau.
919222
The correlation coefficient is a number between -1 and 1 that measures the degree in which values in a sample space are linearly related. Graph the points and see how they are related spatially, if the points form something that looks like a blob  then the correlation coefficient is zero, or there is no linear relationship. If the points form a perfect line with a positive slope, then the correlation coefficient  is  1. If the points form a perfect line with a negative slope, then the correlation coefficient is  -1. So when you graph your sample space make a guess at where the data falls within the range of -1 to 1. Hope this helps.\n\nP.S. Use excel and you can graph those points quickly.
320722
The central limit theorem still applies.\n\nWhen variables are correlated a principal component analysis will find the independent variables for which the CLT applies.