Quantitative Techniques

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Quantitative techniques may be defined as those techniques which provide the decision makes a systematic and powerful means of analysis, based on quantitative data. It is a scientific method employed for problem solving and decision making by the management. With the help of quantitative techniques, the decision maker is able to explore policies for attaining the predetermined objectives. In a conclusive way, the techniques (quantitative) are expected in the final decision composing process.

There are different types of quantitative techniques. The types of techniques are as follows:-

-Permutations and Combinations.

Permutation means arrangement of objects in a definite order. The number of arrangements depends upon the total number of …show more content…

It helps to determine the most economic replacement policy.

Non Linear Programming is actually a technique of programming which actually finds out the finest solution to any type of problem which involves non –linear variables.

Sequencing tool is used to determine a sequence in which given jobs should be performed by minimizing the total efforts.

Quadratic programming technique is designed to solve certain problems, the objective function of which takes the form of a quadratic equation.

Branch and Bound Technique it is a recently developed technique. This is designed to solve the combinational problems of decision making where there are large numbers of feasible solutions. Problems of plant location, problems of determining minimum cost of production etc. are examples of combinational problems.

The following are the important functions of quantitative techniques:
1. To facilitate the decision-making process
2. To provide tools for scientific research
3. To help in choosing an optimal …show more content…

Here we have and
Thus the degrees of freedom for the chi-square independence test is given as

Here we set level of significance.
Using Chi-Square Distribution tables, we find the critical value of right-tailed test for 2 degrees of freedom at level of significance as
Thus the critical region is for this test is obtained as
Under the assumption of the null hypothesis, the test statistics for the chi-square independence test is defined as follows:

By observe that the test statistics is less than the critical value, we fail to reject the null hypothesis. That means the variables are independent. Hence we can conclude that the health condition is independent of Risk in the occupation.

Using regression analysis to estimate the absenteeism by using independent variables as medical issue, number of children and risk his/her occupation.

REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT No_Absents /METHOD=ENTER Medical_issue Childrens Risk

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