line of best fit.
collecting the data
It is better to obtain as much data possible in order to be as accurate as one can be.
A best fit graph to some data is exactly that: it is a line which fits the data best according to some optimality criterion. There is a always a trade off in fitting a line to data: one can change the number of degrees of freedom of the underlying equation, which affects how close the line can get to the data points. With more degrees of freedom, the line can more closely approximate the data. This is not to say that more degrees of freedom are better: with too many degrees of freedom, one is merely fitting to the noise in the measurement of the data, and the line will predict subsequent data poorly, when both interpolating and extrapolating the existing data. This is an example of Occam's Razor: one must pick the simplest model which adequately fits the data.
to gather data from data to create an controlled experiment
to gather data from data to create an controlled experiment
Anand Mehta said yes and this is correct. You will get a SD, for example, if all of the data points are less than one, or if the data points are very close together and there is not much spread in the data..
the line of best fit if you have any more math questions conmtact me at jibleesmaster@yahoo.com
That is not true. It is possible for a data set to have a coefficient of determination to be 0.5 and none of the points to lies on the regression line.
line of fit
Data points that are not close to the line of best fit are called outliers.
It is possible for a data set to have no mode if none of the data points are repeated.
A scatter plot
That will depend on how you have defined "best fit".-- If it means "maximizes the number of points that are right on", then you'll want tojockey the line around so that as many data points as possible fall directly on it.-- If 'best fit' means the sum of the distances by which the line misses all data pointsis as small as possible, then very few of them may actually sit right on the line.
Not enough information
If you plot data points on a graph the rarely will form a straight line. Least squares is a method of finding a line 'close' to all the data points instead of just guessing and drawing a line that looks good. If you have a line, then there is an algebraic formula to find the distance from each point to that line. Then using statistics, you can make the statistically averaged distance from each data point as close as possible to a line. The distances are squared, averaged, and the average of those squared distances may be used to find the regression line.
the average
Is a wriggly curve that goes through each one of them.