Regression analysis is a statistical method used to determine the relationship between variables. It allows one to predict the value of a dependent variable based on the value of one or more independent variables. There are three main uses of regression analysis: prediction, model specification, and parameter estimation. However, accurate prediction, model specification, and parameter estimation require that all relevant variables are accounted for in the data and model. Limitations in data can restrict the use of regression analysis for prediction.

1. Regression Analysis

2. Meaning
• Regression is the study of the nature of relationship between the
variables so that one may be able to predict the unknown value of one
variable for a known value of another variable
• A statistical measure that attempts to determine the strength of the
relationship between one dependent variable and a series of other
changing variables (known as independent variables).

3. Definition
• Regression is the measure of the average relationship
between two or more variables…………According to
M.M. Blair

4. Utility
Uses of Regression Analysis
Three uses for regression analysis are for
prediction
model specification and
parameter estimation.
Regression analysis equations are designed only to make
predictions. Good predictions will not be possible if the model is not
correctly specified and accuracy of the parameter not ensured. However,
accurate prediction and model specification require that all relevant
variables be accounted for in the data and the prediction equation be
defined in the correct functional form for all predictor variables.

5. Continue
Parameter estimation is the most difficult to perform because not only is
the model required to be correctly specified, the prediction must also be
accurate and the data should allow for good estimation. For example,
multicolinearity creates a problem and requires that some estimators
may not be used. Thus, limitations of data and inability to measure all
predictor variables relevant in a study restrict the use of prediction
equations.

6. Difference
Correlation
Regression
Correlation examines the relationship
between two variables using
astandardized unit. However, most
applications use raw units as an input.
Regression examines the relationship
between one dependent variables and
one or more independent
variables. Calculations may us either
raw unit values, orstandardized units as
input.
The calculation is symmetrical, meaning
that the order of comparison does NOT
change the result.
The calculation is NOT symmetrical. So
one variable is assigned the dependent
role (the values being predicted) and
one or more the independent role (the
values hypothesize to impact the
dependent variable).
Correlation coefficients indicate the
strength of a relationship.
Regression shows the effect of one unit
change in an independent variable on
the dependent variable.