Compose a 1000 words essay on Decision Analysis for the US Army. Needs to be plagiarism free!Download file to see previous pages... Analytical decisions are undertaken when there is ample time to comp

Compose a 1000 words essay on Decision Analysis for the US Army. Needs to be plagiarism free!

Analytical decisions are undertaken when there is ample time to compare the facets, in order to choose the most profitable course of direction. The best module amongst the alternatives is taken (See appendix). In this case the supply of weapons is done from the highest bidder and of the best quality. Decision trees This is a tool that allocates data for decision making in form of tree in order to analyze the variable hence make the decision. The tool breaks down the data in logical and simple manner thus easy to understand. Any mistakes made in the data translate to the tree model. Each nodule of the tree represents an attribute, the branches corresponds to an attribute while each leaf assigns a classification. The module is very simple to use and interpret and it can handle both numerical and categorical data. The decision trees can be used for validation analysis as other statistical approach follows (Andrew, 2005). The simple illustration below describes the case of buying or making weapons. A decision is made after thoroughly considering all the uncertainties involved then a decision is made. Cost factor can be used to make the decision and the manufacturing option would be adopted. Nonetheless, other factors need to be considered in real situation analysis e.g. quality, time taken etc. Illustration one: A variable is an element of a problem that is being predicted or determines it. They may change depending on the scope of the problem being analysed. Predictable variables are used to assess the certain conditions while unpredictable variables follow uncertainties hence probability is used. Variables are used in decision analysis to represent value or symbol of the actual attribute being determined. A variable denoted by Y may represent the cost estimates, overheads, revenues etc. When one value of a variable is related to another value of a variable, then the case is said to a correlation between the two variables. Correlation means an inter-relationship. A simple linear regression model is a statistical technique that explores the inter-relations between two or more variables. Linear regression tools have applied in many organisations to forecast for the observed variables. The military may want to determine whether there is a relationship between warring period and weapons use. To do so, the observed data is analysed and the simple line equation is used. Y=a+bx Y in this case represents number weapons used during war X variable shows the number the war reoccurs A and b are constants Table one weapons Time period lasted during the Wars 100000 1 year World war I 300000 1 year World war II 250000 2 years’ War in Afghanistan 85000 1 year War in Kuwait The equation is then extend to determine a and b i.e. 100,000= a+ 1b 250,000=a+2b -150,000= -b 150,000= b Then a=100,000-1b = 100000-150,000 = -50,000 If we were to determine the occurrence of world war III will take 3 years, then the number of weapons needed are: Y=a+bx = -50,000 + 150,000*3 =400,000 weapons The simple regression analysis can be determined by plotting a graph using the same variable to obtain the line of best fit. This can be then be used to forecast on future needs of weapon acquisition for the military. The relationship between the period the war takes the higher the number of weapons needed. The relationship is linear. This model works best when there are two variables under consideration.