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UNIVERSITY CANADA WEST MGMT 660: Leadership and Decision Making DECISION MATRIX AND CARTER RACING Word Count: 1382 Academic Integrity Statement: I, Abdul khaliq Khan, hereby declare that this work is my own and is in accordance with the University Canada West Academic Integrity Policy. I have properly cited all sources used in this paper. E-Signature: Abdul khaliq Khan Date:10/23/2023 Instructor's Name: Course Number:MGMT 660 1

Section 1: Decision Matrix Job Offers: Offer A: Software Engineer at Company Google in San Francisco Offer B: Marketing Manager at Company Amazon in New York Offer C: Financial Analyst at Company Motorola solution in Chicago Decision Criteria and Weights: ( Pugh,1981) Offer A Offer B Offe r C Criteria Weight Rating Score Rating Score Rating Score 1.Salary 3 3 9 2 6 2 6 2.Location 2 2 4 3 6 1 2 3.Company Culture 3 3 9 2 6 1 3 4.Opportunities for Growth 2 3 6 3 6 2 4 5.Work-Life Balance 2 3 6 1 2 2 4 Total Score 34 26 19 Summary: Offer A, held by a Google Software Engineer, scored 34 points, the highest. The company excelled in pay, culture, and career advancement. 2

Race is alternative 1. Race Blown Engine don't finish probability = P(BE) = 0.10. Don't Finish for Other Reasons = P(OR) = 0.05 Top 5 finish probability = 0.10 The probability of finishing outside the top 5 is 0.75. Alternative 2: Don't Race Probability of Not Racing = 1 (No racing costs) Calculate the expected values (EV) for both alternatives: Racing EV (Alternative 1):Racing (Race) = P(BE) * (-$50,000) + P(OR) * (-$50,000) + P(Top 5) * $40,000 + P(Not Top 5) * $20,000 Alternative 2: EV for Not Racing Not Race EV = 1 * (-$15,000) Let's calculate these values: EV(Race) = -$5,000 - $2,500 + $4,000 + $15,000 = $11,500. 1 * (-$15,000) = EV(Not Race). Racing has an anticipated worth of $11,500, whereas not racing has a value of - $15,000. Decision: The predicted profit is positive ($11,500), therefore racing is better than not racing, which would cost $15,000. Possible outcomes Alternative 1: Race Alternative 2: Don’t race Probability Profit Probability Profit Don’t race Not applicable 1 -$15,000 Finish in top 5 0.10 $40,000 Not applicable 4

Finish outside of top 5 0.65 $ 20,000 Don’t finish due to blown engine 0.10 -$15,000 Don’t finish due to other reasons 0.15 - $ 50,000 3) Temperature Data The specified temperature is 40ºF (4ºC). Temperature affects engine performance, especially under race conditions, so this data is crucial. Engine failure may rise in cold weather. However, temperature alone should not determine racing. The expected values calculation above considers profit and probable outcomes. Temperature is one of several factors to consider. An engine may blow if the temperature is too low for the automobile and engine. The anticipated values computation should include this in the probability of a blown engine (P(BE)). Low temperature may increase the risk of a blown engine, making not racing more appealing. The three gasket failures at 53 0 degrees indicate that engine failure is possible even at temperatures somewhat below the acceptable threshold. This should inform the team's racing decision. The data also demonstrates that incorrect engine maintenance increases engine failure risk. In poorly held races, the three 53 0 -degree gasket failures happened. Proper maintenance may reduce engine failure even at temperatures below 65 0 degrees. Racing is complicated and depends on many things. The team must assess risks and benefits before deciding. The data implies that the team should be more cautious about racing below 65 0 degrees and maintain the engine before starting. Some extra thoughts on data: Temperature is strongly correlated with engine failure, although not perfectly. Three gasket failures at 53 0 degrees reveal other engine failure issues. Maintenance may decrease engine failure even at temperatures below 65 0 degrees, according to the study. This is crucial because it reduces engine failure risk even if the team races below 65 0 degrees. 5

REFERENCES: 1. Brittain, J. & Sitkin, S. (1986). Carter racing. Case Study. 2. Borgonovo, E. and Marinacci, M. ( 2015 ). Decision analysis under ambiguity, European Journal Of Operational Research Volume: 244 Issue: 3 Pages: 823-836 DOI: 10.1016/j.ejor.2015.02.001 3. Bar-Eli, M., Azar, O.H., Ritov, I., Keidar-Levin, Y., and Schein, G. (2007). “Action bias among elite soccer goalkeepers: The case of penalty kicks.” Journal of Economic Psychology . 28(5), 606-621. DOI: 10.1016/j.joep.2006.12.001 4. Brittain, J. and Sitkin, S. (2000). Carter RacingLinks to an external site.. David Eccles School of Business University of Utah. 7

5. Cheikes, B. A., Brown, M. J., Lehner, P. E., & Adelman, L. (2004) Confirmation Bias in Complex Analyses. MITRE Center for Integrated Intelligence Systems. Bedford, MA. 6. Pugh, S. (1981). Concept Selection: A Method That Works. In: Hubka, V. (ed.), 'Review of Design Methodology. Proceedings International Conference on Engineering Design, March 1981, Rome,' Zürich: Heurista. p.497-506. 7. https://www.statology.org/expected-value-real-life-examples/ 8. Robbins, S. and Judge, T., (2022). Essentials of organizational behavior. (15th ed.). Pearson 8