There are two types of Regression Modelling; the **Deterministic** Model and the Stochastic Model. **The deterministic** model is discussed below.

**Deterministic Definition**

The word **deterministic** means that the outcome or the result is predictable beforehand, that could not change, that means some future events or results of some calculation can always be predicted and is same, there is no randomness in **deterministic**. For example – Calculation from meter to the centimeter or gram to kilogram, etc. i.e the formula for solving remains the same and does not change randomly. Something is called **deterministic** when all the needs are provided and one knows the outcome of it.

**Deterministic **Model

In** a deterministic** model, when one starts running the model with the same initial condition every time, the result or the outcome is the same. Moreover, a **deterministic** model does not involve randomness; it works accordingly. In the case of the **deterministic **model when some work starts at a particular time that is at the same pace every time, then the output of the model always depends on the initial conditions. For a well-defined linear model, the unique output is produced from a unique input, and in the case of a non-linear model, multiple outputs are produced. This model can be described in different stages of temporal variations viz. time-independent, time-dependent a d dynamic. **A deterministic system** assumes an exact relationship between variables. As a result of this relationship between variables, it enables one to predict and notice how variables affect the other.

For example.

If one assumes that X (Ram) is 4 times taller than Y (Rohan), then the equation will be X = 4Y.

Therefore, the example tells that X can always be identified or determined when Y is known.

This is a type of prediction which is hypothetical because the statement is helping one to identify what would be the outcome if we use a particular Y. Therefore, the deterministic technique also provides reliability in its solution as in the example shown.

So, the **deterministic system **will always be predictable and produce the same output if the starting point or the initial stage remains the same and in the case of any machine, the motion of the model should also be the same every time to get a similar result always.

**A deterministic** **system** does not have any random or probabilistic element, a model is called a **deterministic** model when it is fully known.

## Conclusion

It is to conclude that there are two types of Regression Modelling; they are the Deterministic Model and the Stochastic Model. The word **deterministic **means that the outcome or the result is predictable beforehand; that could not change, that means some future events or results of some calculation can always be predicted and is the same; there is no randomness in **deterministic**.