Computer 9th Chap 4 : Topic 2.13 - Maryam Publications

Computer Chapter 4 : Topic 2.13

Variable Relationships

1 / 9

In a dataset stored for analysis, what is the most accurate explanation of why a relationship between two variables may appear even when no real causal link exists in real-world context?

A) The relationship must always be caused by measurement errors only

B) The relationship may appear due to coincidence, hidden variables, or sampling bias

C) The relationship only exists when data is collected manually

D) The variables always determine each other directly in every dataset

2 / 9

When analyzing two variables in a dataset, which situation best indicates that no meaningful relationship exists between them?

A) Both variables are measured using the same unit

B) Changes in one variable show no consistent pattern in relation to the other

C) One variable directly controls the values of the other

D) Both variables increase together consistently in all observations

3 / 9

Why might two variables show a strong correlation in data analysis but still not have a causal relationship?

A) Because both variables may be influenced by a third hidden factor

B) Because graphs automatically create false relationships

C) Because statistical tools remove all real-world meaning

D) Because correlation always guarantees causation in datasets

4 / 9

In a classroom dataset, students notice that higher ice cream sales are linked with higher drowning cases. What is the most reasonable explanation?

A) The dataset must be incorrect and unusable

B) Ice cream causes drowning directly

C) A third factor like hot weather influences both variables

D) Drowning causes people to eat ice cream

5 / 9

Which statement best explains why relationships between variables must be interpreted carefully in data science?

A) Variables in datasets never interact in real life

B) Relationships may be influenced by randomness or external factors

C) Data always reflects perfect real-world causation

D) Every correlation found in data is scientifically proven

6 / 9

When a dataset shows no relationship between study time and exam scores, what is a logical explanation?

A) Students may use different study methods or have varying abilities

B) Study time always guarantees high scores

C) Exam scores are unrelated to any human factor

D) Data visualization always hides true relationships

7 / 9

What is the most likely reason for an observed relationship that disappears when more data is collected?

A) The variables stopped interacting over time

B) Initial dataset was too small and not representative

C) Data collection always destroys patterns

D) Graphing tools removed the relationship

8 / 9

Why is it dangerous to assume causation from correlation in data analysis?

A) Because datasets cannot contain relationships

B) Because it may lead to incorrect conclusions and decisions

C) Because correlation automatically proves a mathematical error

D) Because all correlations are random

9 / 9

In which situation is a “no relationship” conclusion most valid?

A) One variable increases steadily while the other decreases

B) Both variables form a straight line pattern

C) Data points are randomly scattered with no visible trend in a scatter plot

D) Both variables are measured in percentages

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