Knowledge Check 3 Transcript Part 1 The Challenger Data Point Scenario Text Your predictive risk model, which analyzes thousands of data points, flags aircraft N419HC as low risk However, you learn it just had a near miss incident involving severe engine vibration Digging deeper, you find it's one of five aircraft in the fleet using blades from an alternate supplier This supplier information was not a variable in your original model Factoid This is an example of a potential 'confounding variable' The supplier type may be the real cause of risk, creating a misleading relationship between the variables your model is using and the actual outcome Displayed Data Standard Supplier (15 Aircraft) 0 3 Avg Vibration Reports Month Alternate Supplier (5 Aircraft) 1 28 Avg Vibration Reports Month Note N419HC alone had 3 2 reports month, while the other four averaged 0 8 Question This new data challenges your model What is your most appropriate first step D) Formulate and conduct a statistical test to see if the vibration rate for the alternate supplier fleet is significantly different from the standard fleet Feedback (Correct) A Data Driven First Step Excellent The core question is whether the supplier matters A hypothesis test will give you a statistical answer to that question This is the most rigorous and defensible first step to understanding if you have a systemic problem or a one off issue Part 2 The Statistical Finding Scenario Text Your first step was correct You run an independent samples t test to compare the average monthly vibration reports between the two supplier groups Displayed Data Test Result The p value is 0 02 Interpretation Since p Factoid A t test is used to determine if there is a significant difference between the means of two groups By getting a low p value, you are rejecting the null hypothesis that the two suppliers have the same average vibration rate Question You've proven the supplier matters What is the most critical modeling action to take now B) Add Supplier Type as a new variable (feature) to your existing model and retrain it on the entire dataset Feedback (Correct) Improving the Model Holistically Perfect By adding Supplier Type as a feature, you are teaching the model about this new risk factor Retraining on all the data allows the model to learn the relative risk of each supplier and apply that knowledge to the entire fleet, making it more accurate and robust Part 3 The Holistic Recommendation Scenario Text You've successfully retrained your model with Supplier Type as a key feature The new model now correctly flags N419HC as high risk and, importantly, elevates the risk profile for the other four aircraft from the alternate supplier, though not as high as N419HC Factoid This process is called 'Feature Engineering'creating new input variables from existing data to improve a model's performance It's one of the most crucial steps in applied machine learning Displayed Data Result Your model is now more accurate and accounts for the previously hidden risk You have a defensible, data driven basis for action Question With an improved model and a clear understanding of the risk, what is your final, comprehensive recommendation B) Ground N419HC for immediate inspection place the other four on an accelerated inspection schedule and formally adopt the new model Feedback (Correct) A Nuanced, Risk Based Response This is the ideal recommendation It addresses the immediate, highest risk (N419HC), mitigates the intermediate risk (the other four aircraft), and implements a long term systemic improvement (adopting the new model) It's a comprehensive, data driven solution Factoid An Airworthiness Directive (AD) is a legally enforceable regulation issued by the FAA to correct an unsafe condition in a product (aircraft, engine, propeller, or appliance) Summary Box The Correct Analytical Path Challenge the Model You respected the anomalous data instead of dismissing it Validate with Statistics You proved a hidden variable was a significant factor Improve the Model You incorporated the new finding to make the model more powerful Recommend Tiered Action Your final recommendation was both immediate and nuanced, addressing the entire risk spectrum Share your experience from the knowledge check and present your perspective on the results Identify as many factoids that you were able to find in your individual scavenger hunt while completing the knowledge check scenario Of the embedded factoids, carry out some simple research using the Internet to provide real world applications Be sure to cite all sources

Question

Knowledge Check 3 Transcript Part 1  The Challenger Data Point Scenario Text  Your predictive risk model, which analyzes thousands of data points, flags aircraft N419HC as  low risk   However, you learn it just had a near miss incident involving severe engine vibration  Digging deeper, you find it's one of five aircraft in the fleet using blades from an alternate supplier  This  supplier  information was not a variable in your original model  Factoid  This is an example of a potential 'confounding variable'  The supplier type may be the real cause of risk, creating a misleading relationship between the variables your model is using and the actual outcome  Displayed Data  Standard Supplier (15 Aircraft)  0 3 Avg  Vibration Reports   Month Alternate Supplier (5 Aircraft)  1 28 Avg  Vibration Reports   Month Note  N419HC alone had 3 2 reports month, while the other four averaged 0 8  Question  This new data challenges your model  What is your most appropriate first step   D) Formulate and conduct a statistical test to see if the vibration rate for the alternate supplier fleet is significantly different from the standard fleet  Feedback (Correct)  A Data Driven First Step   Excellent  The core question is whether the supplier matters  A hypothesis test will give you a statistical answer to that question  This is the most rigorous and defensible first step to understanding if you have a systemic problem or a one off issue  Part 2  The Statistical Finding Scenario Text  Your first step was correct  You run an independent samples t test to compare the average monthly vibration reports between the two supplier groups  Displayed Data  Test Result  The p value is 0 02  Interpretation  Since p Factoid  A t test is used to determine if there is a significant difference between the means of two groups  By getting a low p value, you are rejecting the  null hypothesis  that the two suppliers have the same average vibration rate  Question  You've proven the supplier matters  What is the most critical modeling action to take now  B) Add  Supplier Type  as a new variable (feature) to your existing model and retrain it on the entire dataset  Feedback (Correct)  Improving the Model Holistically   Perfect  By adding  Supplier Type  as a feature, you are teaching the model about this new risk factor  Retraining on all the data allows the model to learn the relative risk of each supplier and apply that knowledge to the entire fleet, making it more accurate and robust  Part 3  The Holistic Recommendation Scenario Text  You've successfully retrained your model with  Supplier Type  as a key feature  The new model now correctly flags N419HC as  high risk  and, importantly, elevates the risk profile for the other four aircraft from the alternate supplier, though not as high as N419HC  Factoid  This process is called 'Feature Engineering'creating new input variables from existing data to improve a model's performance  It's one of the most crucial steps in applied machine learning  Displayed Data  Result  Your model is now more accurate and accounts for the previously hidden risk  You have a defensible, data driven basis for action  Question  With an improved model and a clear understanding of the risk, what is your final, comprehensive recommendation  B) Ground N419HC for immediate inspection  place the other four on an accelerated inspection schedule  and formally adopt the new model  Feedback (Correct)  A Nuanced, Risk Based Response   This is the ideal recommendation  It addresses the immediate, highest risk (N419HC), mitigates the intermediate risk (the other four aircraft), and implements a long term systemic improvement (adopting the new model)  It's a comprehensive, data driven solution  Factoid  An Airworthiness Directive (AD) is a legally enforceable regulation issued by the FAA to correct an unsafe condition in a product (aircraft, engine, propeller, or appliance)  Summary Box  The Correct Analytical Path Challenge the Model  You respected the anomalous data instead of dismissing it  Validate with Statistics  You proved a hidden variable was a significant factor  Improve the Model  You incorporated the new finding to make the model more powerful  Recommend Tiered Action  Your final recommendation was both immediate and nuanced, addressing the entire risk spectrum  Share your experience from the knowledge check and present your perspective on the results  Identify as many  factoids  that you were able to find in your individual  scavenger hunt  while completing the knowledge check scenario  Of the embedded  factoids,  carry out some simple research using the Internet to provide real world applications  Be sure to cite all sources

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Question: Knowledge Check 3 Transcript Part 1: The Challenger Data Point Scenario Text: Your predictive risk model, which analyzes thousands of data points, flags aircraft N419HC

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