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Quantitative Reasoning Midterm Exam Test Questions – Advanced Sampling, Rates, Confounding, Association, and Variable Types (GEA1000 NUS Practice)

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This document compiles challenging midterm questions on quantitative reasoning concepts relevant for GEA1000 at the National University of Singapore. Topics include interpreting contingency tables, Simpson’s paradox, confounding variables, association analysis, and probability calculations. It covers classification of variable types (ordinal, nominal, numerical), random assignment proportions in experiments, marginal and joint rate calculations, as well as interpreting significance of rates using the basic rule of rates and symmetry rules. Questions also integrate real-world examples, such as COVID-19 statistics, food delivery punctuality, and medical trials, providing a realistic and practical approach to mastering key concepts for exams.

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QUANTITATIVE REASONING WITH DATA
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QUANTITATIVE REASONING WITH DATA
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QUANTITATIVE REASONING WITH DATA

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Uploaded on
July 2, 2025
Number of pages
9
Written in
2024/2025
Type
Exam (elaborations)
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Questions & answers

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QUANTITATIVE REASONING WITH
DATA (GEA 1000) SEMISTER 1
MIDTERM TEST (suggested solutions)
2025-2026 UNIVERSITY OF SINGAPORE

, GEA1000 Quantitative Reasoning with Data
2025-2026 Semester 1
Midterm Test
Suggested Solutions


Q1

The table below shows male and female patients undergoing two treatment types, X or Y. The outcome of the
treatment is designated as either successful or unsuccessful. The success rates of the respective treatments across
genders are also calculated.

Male Female
No. of Patients No. of Rate of No. of Patients No. of Rate of
Treated Success Success Treated Success Success
Treatment X ? ? 50% 40 32 80%
Treatment Y ? ? ? ? ? 60%
Total 100 50 50% ? ? ?


Unfortunately, some of the data is missing. We know that all missing values are non-zero. Which of the following
statements is necessarily true?

(I) Simpson’s Paradox is observed when the subgroups of Treatment X and Treatment Y are combined, when
considering the relationship between gender and outcome.
(II) Treatment type is a confounder between the variables gender and outcome.

a) Only (I)

b) Only (II)

c) Both (I) and (II)

d) Neither (I) nor (II)

Explanation
By the basic rule of rates, among males, rate of success given Treatment Y must be 50%. By the basic rule of rates,
among females, overall rate of success must be between 60% and 80%. Simpson’s Paradox is not observed when the
subgroups are combined, hence (I) is false.
Depending on the data, treatment type may or may not be a confounder.
For the following given set of values in the table, rate(X|male) = 80/100 = 40/50 = rate(X|female), so treatment type
is not associated with gender. Treatment type is not necessarily a confounder hence (II) is false.

Male Female
Undergone Successful Rate of Undergone Successful Rate of
Treatment Success Treatment Success
Treatment X 80 40 50% 40 32 80%
Treatment Y 20 10 50% 10 6 60%
Total 100 50 50% 50 38 >60% and
<80%

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