What is the most suitable line?
In mathematics, the line is the best line customization that can be drawn on points in the data scattering chart. The scatters are formed when two properties of something, such as a day and a high temperature per day, are connected. The line best fit best describes the points on the scattering chart when the average difference between where the line is drawn, and the nearest point is the least. This is easy to control by the smallest square method. Equations are sometimes used to describe lines as a function when only one point is related to the point on the best customization line.
It is important to understand that all lines are inclined and intercept. The tendency describes how quickly the line changes between any two relationships. The intercept describes a point where part of the relationship becomes zero if the line has been extended to this point.
The development of a good line for assembly is useful because it allows predictions when the data is not presented. If only two body one -line can be drawn with a ruler asabout a straight line between two points. With just two points, the line is best accurate and may not be checked. He can now display the exact position of the relationship that would land between two points.
The distraction graph of two relations is the way most data is recorded in statistics. Most scattering charts have many points and the use of a ruler to draw the most suitable line is no longer the right technique. If the relationship is considered for the first time arranged, the line of best customization will still be a straight line, but this line does not have to touch any points.
The smallest square method determines whether one line replies to data better than another. This does by seeing whether the difference between each point and the point that predicts is the smallest possible difference. The difference in differences provides a number that reproachs how well the line corresponds to the data. Other lines could get a lower value and become a new lineBest fit in a process called linear regression.
Not every line is a line, many of them are curves and even three -dimensional. Multiple linear regression is a statistical technique used to find a line most suitable for data that does not follow a line. The regression concerns the curve and surface adaptation, but even for these much harder use of the line of the most suitable adaptation to check and compare the results still uses the smallest square method.