Question

Hello,

please slove it,

Using the definitions found in Chapter 1 of Quantitative Analysis, the Internet, and your own personal experiences, make notes on and post one example of each of the following to the class Discussion Board topic "Deterministic and Probabilistic Models".

A deterministic model;

A probabilistic model; and

A situation in which you could use post optimality analysis (also known as sensitivity analysis).

Submission Instructions

Let        r^2= 0.6719  so the percentage of  statistical model explain about the variation in selling price is ?

thx.

Answer 1

Answer :

A Deterministic Model

1. A deterministic model is a model which gives the same exact results for a particular set of inputs, no matter how many times they are recalculated then.
2. A deterministic model assumes certainty in all aspects.
3. It gives a single outcome for an event .
4. The outcomes are determined through known relationships among states and events in an experiment or the parameter values .
5. No randomness is involved in deterministic analysis.
6. since there is no randomness , the deterministic model exactly determines the output.
7. In deterministic model, we have all of the data necessary to predict (determine) the outcome with 100% certainty.
8. Examples:
   • Suppose rolling a fair die : each number on a six sided die has same odds
   • Determing the bank balance after specific period given the rate of interest.
   • Relationship between direction and slope of a line
   • Relationship between radius and area of a sphere.

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A Probabilistic Model

1. A probabilistic model is based on the theory of probability.
2. In a probabilistic model if we give input data and the parameter values, the model gives us a variable or different output , and we get a different result due to randomness is present here.
3. A probabilistic model gives a probability distribution as a solution.
4. Random variables from normal distribution, binomial distribution, poisson distribution, geometric distribution, etc are all probabilistic models.
5. It was introduced by Paul Erdős.
6. Probabilistic Model aims to provide a more realistic estimate of the subject unser study.
7. Examples :
   • Normal distribution model
   • Markov Chains
   • Suppose we have to model the distribution of temperatures of our area to determine whether it will rain or not is alao a type of probabilistic model.

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A situation in which you could use post optimality analysis (also known as sensitivity analysis)

Sensitivity Analysis is used where if the situation for predicting the outcome or result becomes different as compared to that of the key predictions.

Sensitvity analysis helps us understand the relationship between input parameters and outputs It is also useful for  testing the robustness of the output and determining optimal parameter values in the model.

• Sensitivity analysis can be used to find the effect of working capital on the company's profit margin.

Here an analyst can use sensitivity analysis ti analyze this situation.

• sensitivity analysis is also used in tisk management .

For e.g. it helps in identifying which task's duration has a highest and strongest correlation with the finish time of a project

The following two situations are an example where post optimality or sensitivity analysis can be used.

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[To express 0.6719 in percentages we multiply it by 100]

If r^2 = 0.6719 then the percentage of the variation explained by statistical model in selling price is 67.19 %

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