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ITIS 1P97 (18)
Lecture

# ITIS 1st MID.docx

5 Pages
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School
Department
Information Technology Information Systems
Course
ITIS 1P97
Professor
Dipanjan Chatterjee
Semester
Winter

Description
Ishani Sinha 1 st ITIS 1 MID-TERM Chapter 1 The Modeling Process -Quantitative Business Modeling 1. Opportunity/ problem recognition (most important step) -determining its magnitude, defining it precisely. -problems are flipside of opportunities -should carefully investigate the situation of concern. 2. Model Formulation (most time consuming steps according to experts) -transforming the complex opportunity or problem into a mathematical formula -the simpler the model, easier the manipulations and the solution but the less representative it will be of the real life problem. -most important is determining the components and key variables of the model when influence diagram is drawn. 3 basic components: independent, dependent, and uncontrolled variables. 3. Data Collection 4. Analysis of the Model -how well the model represents reality -are they empirically accurate -representative of system’s behavior under real world situation 5. Implementation and Project Management 3 activities -communicating the results of the study and the modeler’s recommendation -actually applying managerially approved action through the project management -evaluate the results Benefits: • Easier than manipulating real system • Cost of mistakes is smaller • Enables compression of time • Helps managers understand tradeoffs between different approaches to a management decisions. • Enables quick identification and analysis of a very large # of possible situation • Aids handling large amount of information Drawbacks: • Time consuming, expensive • Reluctant to accept results • Obtaining necessary data might be difficult • Difficult to access uncertainties • Perception that it actually eliminates the risks and uncertainties • Results may be used without appropriate validation o Validation - establishes how well the model represents reality Chapter 2 Types of variables, observations, & their properties Approaches to data collection: groups or cross-sectional and time series Ishani Sinha 2 Types of Observations • Categorical o Nominal o Ordinal • Numerical o Discrete o continuous Definitions of probability: Mean Variance Standard Deviation Population Sample Excel can calculate both – variance of population and variance of sample Excel always calculates sample – standard deviation of population and standard deviation of sample Statistical Displays / Frequency Tables: When are they used? • Bar chart : ordinal data, arranged in order • Pareto Chart: nominal data, arranged highest to lowest • Histogram: numerical data, no space between bars 3 Types of probabilities Ishani Sinha 3 1. Subjective Probability – individual information and belief 2. Logical Probability – physical or deterministic phenomena 3. Experimental – frequencies based on trials 5 Types of events 1. Independent events occurrence of one event does not affect the probability of occurrence of the others. (head / tails of a coin) 2. Dependent events occurrence of one event affects the occurrence of the next event. (drawing a second diamond card from the deck) 3. Mutually exclusive events occurrence of one precludes the occurrence of the others. (birth of a child -> “it’s a boy” or “it’s a girl”) 4. Collectively exhaustive if one event occurs, then the set of events needs to occur (when rolling a die, 1, 2, 3, 4, 5, 6 must occur for the event to occur) 5. Complements Law (p,q) the probability of an event not occurring (p) then the probability of the opposite event to occur is (1-p) (if p=0.6 then q=0.4) 2 Formulas (‘AND’ & ‘OR’) 1. The Multiplication Rule Joint Probability for dependent events Conditional probability for dependent events Joint Proba
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