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Logic Modeling Approaches

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Logic Modeling Approaches

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    1. Logic Modeling Approaches Structured English, Decision Tree and Decision Table

    2. Structured English Decision trees Decision tables

    3. Structured English Modified form of English used to represent process details There is no right way to write pseudo code. Just have a method and be consistent throughout document

    4. Five conventions for Structured English Structures: sequential, decision, iterations. Capitalize keywords: IF, THEN, ELSE, DO, DO WHILE, DO UNTIL, PERFORM Indent blocks to show hierarchy (nesting). Underline the words or phrases to indicate that they have a specialized, reserved meaning. Be careful when using "and" and "or" as well as "greater than" and "greater than or equal to" and other logical comparisons.

    5. Three Parts to Structured Programs Sequence Conditions Repetition

    6. Structured English Example Example: For each LOAN ACOUNT NUMBER in the LOAN ACCOUNT FILE do the following steps: If the AMOUNT PAST DUE > 0 then While there are LOAN ACCOUNT NUMBER for The CUSTOMER NAME do the following steps: Sum the OUTSTANDING LOAN BALANCEs Sum the MINIMUM PAYMENTs Sum the PAST DUE AMOUNTs Report the CUSTOMER NAME, LOAN ACCOUNTs on OVERDUE CUSTOMER LOAN ANALYSIS.

    7. Structured English Example

    8. Decision Trees A graphical representation of a decision situation Decision trees present a clear, logical model that can be understood easily

    9. Four major steps in building Decision Trees: Identify the conditions Identify the outcomes (condition alternatives) for each decision Identify the actions Identify the rules.

    10. Decision Trees Symbols Two main components : Decision points represented by nodes Actions represented by ovals

    11. Decision Trees Example

    12. Decision Trees Example: fruit classification Suppose a classification problem has nominal data. Example: color : {green, yellow, red}. size : {big, medium, small} In this case we have to move away from the idea of continuous probability distributions.

    13. Decision Trees Example: Iris classification

    14. Another Example

    15. Decision Tables A matrix representation of the logic of a decision. A tabular format for defining the rules that choose a particular action to perform based on the values of certain parameters

    16. Decision Tables A decision table is a table composed of rows and columns, separated into four separate quadrants. Help analysts ensure completeness and accuracy

    17. Four main problems Incompleteness Impossible situations Contradictions Redundancy

    18. Decision Tables : Grading policy Professor Nyuen Trick has fairly complex grading policy consisting of three conditions and five actions. The three conditions are average exam score, individual project, and homework assignments. The five actions are the assignment of A, B, C,D, or F as grades. The grading policy includes the following points. The average exam score is determined by summing the exam scores and dividing by the number of exams. The grading scale is as follows: 90-100=A, 80-89=B, 70- 79=C, 60-69=D, and below 60=F. To receive the grade in the range corresponding to the average exam score, the student must turn in all homework assignments and receive a “pass” on the individual project. If the student receives a “fail” on the individual project and turns in all the homework, the student receive one grade lower than the average exam score. If the student does not turn in all the homework, the student fails the class, regardless of the average exam score and the grade on the individual project.

    19. Developing Decision Tables (1)

    20. Developing Decision Tables (2)

    21. Developing Decision Tables (3)

    22. Developing Decision Tables (4)

    23. Developing Decision Tables (5)

    24. Developing Decision Tables (6)

    25. References Hoffer, Jeffrey A., Modern systems analysis and design, 3nd edition, Addisson Wesley Longman Inc., 2002

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