OLS means Ordinary Least Square. It is the best and most
approximate estimation method used for simple linear regression
models. If the linear models meets the OLS conditions, such linear
models can be said to get best possible estimates. WIth a data, OLS
will fit a function closely. For that, the sum of squared errors
from the data have to be minimized. Now-a-days scientists and
sociologists used to do regression very scarcely, that too using
one independable variable. Whereas, OLS performs the same with
number of variables.
In Simple linear regression models,
- They help in exploring bivariate as well as multivariate
variable relationships. Here, one variable depends on another or
combined variables.
- Correlation coefficient figures out two variables are linked
with other, however does not reveal the kind of relationship.
- Relation between political science and economics variables are
not accurate. Only the definition will be correct.
- This helps to identify the average relationships which could
not be understood by just a one-round view of the data. Intense
formulation of structural components of hypothesized variables
relationship which are random. As an example is the positive
relationship existing between unemployment and government
spending.