What if Analysis,Goal Seek Analysis,Sensitivity Analysis,Optimization Analysis,Data Mining
- 2. CONTENT WHAT IF ANALYSIS SENSITIVITY ANALYSIS OPTIMIZATION ANALYSIS DATA MINING 01 02 03 04
- 4. Makes hypothetical changes to problem and observes impact on the results
- 5. Types of What If Analysis Scenario Analysis Data Tables Analysis What-If Analysis is the process of changing the values in cells to see how those changes will affect the outcome of formulas on the worksheet. Goal Seek Analysis
- 6. Types Cont. Goal Seek Analysis Data tables Analysis It evaluates the expected value of a proposed investment or business activity. The process of finding the correct input when only the output is known. A data table is a range of cells in which you can change values in some of the cells & come up with different answers to a problem Scenario Analysis
- 7. Sensitivity Analysis A sensitivity analysis is a technique used to determine how different values of an independent variable impact a particular dependent variable under a given set of assumptions. This technique is used within specific boundaries that depend on one or more input variables, such as the effect that changes in interest rates have on bond prices. Example Eagle Airlines
- 8. Partial Sensitivity Analysis Best-case and worst-case scenarios Break-even analysis 01 02 03 Types Of Sensitivity Analysis
- 9. Types cont. In a partial sensitivity analysis, you select one variable, change its value while holding the values of other variables constant. Best- and worst-case scenarios establish the upper (best-case) & lower (worst-case) boundaries of a cost-benefit study’s results. This type of se nsitivity analysis shows how a broad range of a program or policy’s pos sible outcomes affect the bottom line If you are unable to estimate a policy’s most likely effects or cannot find comparable studies to help determine its best-case and worst-case sce narios, Partial Sensitivity Analysis Best-case & worst-case scenarios Break-even analysis
- 10. Sensitivity Analysis Advantages Disadvantages . Simplicity Directing Management Efforts Ease of being Automated As a quality Check It does not provide clear cut results Not a solution in standalone form
- 11. Optimization Analysis Then one or more other variables are changed repeatedly, subject to the Specified constraints, until the best values for the target variables are discovered It Is a more complex extension of goal seeking analysis. . Instead of setting a specific target value for a variable, the goal is to find the optimum value for one or more target variables, given certain constraints. Optimization Analysis
- 12. Optimal Value "Optimal" simply means best. In some scenarios, it means the cheapest lunch In other cases it could mean the maximum value of nutrition Either way, it's the value that's somehow considered the best.
- 13. Purpose of Optimization analysis Goal is to find the optimum value for one or more target variables given certain constraints One or more other variables are changed repeatedly until the best values for the target variables are discovered Finding the optimum value for one or more target variables
- 14. Optimization methods One or more other variables are changed repeatedly until the best values for the target variables are discovered 25% 30% 15% 35% Optimization analysis is quite similar to the goal seeking analysis in term of aiming to attain certain goal with given constraints
- 15. Data Mining What is Data Mining Data Mining What is not Data Mining The task of discovering interesting pattern from large amount data DATA + MINING Query Processing
- 16. Data Mining Architecture Get a modern PowerPoint Presentation that is beautifully designed. Easy to change colors, photos and Text. You can simply impress your audience and add a unique zing and appeal to your Presentations. Get a modern PowerPoint Presentation that is beautifully designed. Easy to change colors, photos and Text. You can simply impress your audience and add a unique zing. Your Text Here You can simply impress your audience and add a unique zing. Your Text Here
- 17. 3 Steps Data Mining Process 01 02 03 Exploration Model building and validation Deployment
- 19. 01 02 Privacy Issues Misuse of information Data Mining
- 20. Conclusion
- 21. Thank you