It tells fully about hypothesis testing and that steps,,

1. HYPOTHESIS TESTING
 Hypothesis testing also calledas methodology of econometrics.
 Hypothesis is a statement or assumption that is yet to be proved.
 Simple Hypothesis:
When a hypothesis specifies all the parameters of a probability
distribution , it is known as simple hypothesis.
 Composite Hypothesis:
When a hypothesis specifies only some of the parameters of a
probability distribution , it is known as Composite Hypothesis.
 Steps in hypothesis testing:
1. Statement of Null hypothesis.
2. Specification of the mathematical model
3. Specification of the econometric model
4. Data collection
5. Hypothesis Testing
6. Estimation of the econometric model
7. Forecasting and Prediction
8. Using the model for agricultural Policies

2. 1) Statement of Null hypothesis:
I. Null Hypothesis-Ho :
For applyingthe test of significance we first set up a hypothesis-
a definite statement about the population parameters .Such a hypothesis
which is usuallya hypothesis ofno-difference is called null hypothesis and it is
denoted by Ho
Example:
Ho- There is no significant difference between the two sample means (ie)
µ1=µ2.
II. Alternate Hypothesis-H1:
Any hypothesis which is complementaryto the null hypothesis
is called an alternative hypothesis , usually denoted by H1.
Example:
There is a significance difference between two sample means.
Symbolically,
H1: μ1≠μ2 (two sided or directionless alternative)
If the statement is that A gives significantly less than B (or) A gives significantly
more yield than B. Symbolically,
H1: μ1 < μ2 (one sided alternative-left tailed)
H1: μ1 > μ2 (one sided alternative-right tailed)
2) Specification of the Mathematical Model :

3. This is where the algebra enters. We need to use mathematical
skills to produce an equation. Assume a theory predicting that more schooling
increases the wage. In economic terms, we say that the return to schooling is
positive. The equation is:
Y=β1+β2X
β1=intercept β2=slope
3) Specification of the Econometric Model :
Here, we assume that the mathematical model is correct but we need
to account for the fact that it may not be so. We add an error term, u to the
equation above. It is also called a random (stochastic) variable.The
econometric equation is:
Y=β1+β2X+u
β1=intercept β2=slope u=error term
4) Data Collection :
Data can be collected by using sampling methods or experiments.
Data
The information collected through censuses and surveys or in a routine
manner or other sources is called a raw data.
There are two types of data

4. 1. Primary data
2. Secondary data
5) Testing of Hypothesis :
Once the hypothesis is formulated we have to make a decision
on it. A statistical procedure by which we decide to accept or reject a
statistical hypothesis is calledtesting of hypothesis.
6) Estimation of the Econometric model :
Here, we quantify β1 and β2
i.e. we obtain numerical estimates. This is done by statistical technique
called regression analysis.
Example:
Y=12.50+0.6X+u
7) Forecasting and Prediction :
If the hypothesis testing was positive, i.e. the theory was concluded
to be correct, we forecast the values of the wage by predicting the values of
education.
Example:
Y=12.50+0.6X
X=10 means then Y=18.50 it is Forecasting

5. Y=20 means then X=14.1 it is Prediction
12) Use for Policy Recommendation :
Lastly, if the theory seems to make sense and the econometric
model was not refuted on the basis of the hypothesis test, we can go on to use
the theory for policy recommendation
Example:
Using the model for agricultural Polices.