This preview shows page 1. Sign up to view the full content.
Unformatted text preview: makes possible
estimation or prediction.
9.1.1 Definition:
Regression is the measure of the average relationship
between two or more variables in terms of the original units of the
data.
9.2 Types Of Regression:
The regression analysis can be classified into:
a) Simple and Multiple
b) Linear and Non –Linear
c) Total and Partial
a) Simple and Multiple:
In case of simple relationship only two variables are
considered, for example, the influence of advertising expenditure
on sales turnover. In the case of multiple relationship, more than
218 two variables are involved. On this while one variable is a
dependent variable the remaining variables are independent ones.
For example, the turnover (y) may depend on advertising
expenditure (x) and the income of the people (z). Then the
functional relationship can be expressed as y = f (x,z).
b) Linear and Nonlinear:
The linear relationships are based on straightline trend, the
equation of which has nopower higher than one. But, remember a
linear relationship can be both simple and multiple. Normally a
linear relationship is taken into account because besides its
simplicity, it has a better predective value, a linear trend can be
easily projected into the future. In the case of nonlinear
relationship curved trend lines are derived. The equations of these
are parabolic.
c) Total and Partial:
In the case of total relationships all the important variables
are considered. Normally, they take the form of a multiple
relationships because most economic and business phenomena are
affected by multiplicity of cases. In the case of partial relationship
one or more variables are considered, but not all, thus excluding the
influence of those not found relevant for a given purpose.
9.3 Linear Regression Equation:
If two variables have linear relationship then as the
independent variable (X) changes, the dependent variable (Y) also
changes. If the different values of X and Y are plotted, then the two
straight lines of best fit can be made to pass through the plotted
points. These two lines are known as regression lines. Again, these
r...
View
Full
Document
This note was uploaded on 01/18/2014 for the course BUS 100 taught by Professor Moshiri during the Winter '08 term at UC Riverside.
 Winter '08
 Moshiri
 Business

Click to edit the document details