While researching a topic, a student has taken the following notes:Dr. Sarah Chen developed a new statistical model for predicting...
GMAT Expression of Ideas : (Expression) Questions
While researching a topic, a student has taken the following notes:
- Dr. Sarah Chen developed a new statistical model for predicting earthquake damage in 2019.
- Her model requires processing massive datasets with over 10 million data points.
- The computational analysis takes approximately 72 hours to complete on standard hardware.
- In 2021, Dr. Marcus Thompson created an alternative statistical model for flood damage prediction.
- Thompson's model also processes extensive datasets containing over 10 million data points.
- His computational analysis similarly requires about 72 hours of processing time.
- Both models have been adopted by emergency management agencies nationwide.
The student wants to emphasize a similarity between the two statistical models. Which choice most effectively uses relevant information from the notes to accomplish this goal?
Chen's and Thompson's models both require extensive computational resources, processing over 10 million data points and taking approximately 72 hours to complete.
Although both models have been adopted by emergency management agencies, Chen focuses on earthquake prediction while Thompson addresses flood prediction.
Chen developed her earthquake model in 2019, while Thompson created his flood prediction model two years later in 2021.
Chen's model predicts earthquake damage and Thompson's predicts flood damage, showing how statistical modeling applies to different natural disasters.
Step 1: Decode and Map the Passage
Part A: Create Passage Analysis Table
| Text from Passage | Analysis |
|---|---|
| Dr. Sarah Chen developed a new statistical model for predicting earthquake damage in 2019. |
|
| Her model requires processing massive datasets with over 10 million data points. |
|
| The computational analysis takes approximately 72 hours to complete on standard hardware. |
|
| In 2021, Dr. Marcus Thompson created an alternative statistical model for flood damage prediction. |
|
| Thompson's model also processes extensive datasets containing over 10 million data points. |
|
| His computational analysis similarly requires about 72 hours of processing time. |
|
| Both models have been adopted by emergency management agencies nationwide. |
|
Part B: Provide Passage Architecture & Core Elements
Visual Structure Map:
- CHEN'S MODEL (2019)
- Purpose: Earthquake damage prediction
- Data: 10M+ data points
- Processing: 72 hours
- THOMPSON'S MODEL (2021)
- Purpose: Flood damage prediction
- Data: 10M+ data points
- Processing: 72 hours
- SHARED OUTCOME
- Both adopted by emergency agencies
Main Point: Two researchers developed statistical models for different natural disasters that share similar computational requirements and both achieved practical adoption.
Argument Flow: The notes first present Chen's earthquake model and its computational specifications, then introduce Thompson's flood model with nearly identical computational requirements, and conclude by noting both have achieved real-world adoption by emergency management agencies.
Step 2: Interpret the Question Precisely
What's being asked? Which choice most effectively emphasizes a similarity between the two models using relevant information from the notes.
What type of answer do we need? A statement that highlights shared characteristics rather than differences.
Any limiting keywords? 'similarity,' 'most effectively,' 'relevant information from the notes'
The student's goal is specifically to 'emphasize a similarity' between the models, so we need a choice that focuses on what they have in common rather than what makes them different.
Step 3: Prethink the Answer
- Looking at our analysis, the key similarities between these models are:
- Both process massive datasets (over 10 million data points)
- Both require similar processing time (approximately 72 hours)
- Both have been adopted by emergency management agencies
- The right answer should focus on these shared characteristics rather than highlighting their differences (earthquake vs. flood prediction, or different development years)
- Since the question asks to 'emphasize a similarity,' the most effective approach would be to directly state what the models have in common
- So the right answer should highlight the computational similarities—the shared processing requirements that make these models comparable despite their different applications
Chen's and Thompson's models both require extensive computational resources, processing over 10 million data points and taking approximately 72 hours to complete.
[Note: No choices were provided in the solution content to analyze]
Although both models have been adopted by emergency management agencies, Chen focuses on earthquake prediction while Thompson addresses flood prediction.
[Note: No choices were provided in the solution content to analyze]
Chen developed her earthquake model in 2019, while Thompson created his flood prediction model two years later in 2021.
[Note: No choices were provided in the solution content to analyze]
Chen's model predicts earthquake damage and Thompson's predicts flood damage, showing how statistical modeling applies to different natural disasters.
[Note: No choices were provided in the solution content to analyze]