Types of regression and linear regression equation
The term regression was first used as a statistical concept in 1877 by Sir Francis Galton. 2.
Regression determines ‘cause and effect’ relationship between variables, so it can aid to the decision-making process. 3.
It can only indicate how or to what extent variables are associated with each other. 4.
There are two types of variables used in regression analysis i.e. The known variable is called as Independent Variable and the variable which we are trying to find out or predict is the dependent variable. 5.
To increase accuracy level; we can increase the no. of independent variables. 6.
To determine a relationship between two variables is to examine the graph of the observed ( or known )data. This graph is called as a ‘Scatter diagram’. a.
Direct Linear relationship
Here as Y increases, X also increases. It is because of high degree of association of data.
b) Inverse linear relationship
In this relationship as Y decreases X increases so it called as inverse linear relationship C) Direct curvilinear relationship
In this diagram it shows a positive curvilinear relationship between X and Y axes. The values of Y increases as X increases; but this increase tapers off beyond certain values of X. This you can say it is “Learning curve”. The employees of many industries, experience learning curve; that is as they produce new product, the time time required to produce one unit is reduced by some fixed proportion as the total no. of units doubles. E.g. Aviation Industry, as manufacturing time per unit for a new aircraft tends to decrease by 20 per cent each time the total no. of completed new planes doubles.
d) Inverse Curvilinear relationship
e) Inverse linear with more scattering
e) In this diagram, it shows widely scattered patterns of points. The wider scattering indicates that there is a lower degree of association between Independent and dependent variable....
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