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Assertion and Reasoning questions:
Direction: Questions 2-3, consist of two statements – Assertion (A) and Reasoning (R). Answer these questions
by selecting the appropriate option given below:
a. Both A and R are true and R is the correct explanation of A.
b. Both A and R are true but R is not the correct explanation of A.

c. A is true but R is false.
d. A is false but R is true.
2. Assertion (A): Hierarchical models allow multiple parents for each child.

Reason (R): These models use a tree-like structure with multiple links between nodes.
3. Assertion (A): The ER model is useful for planning the structure of a database.

Reason (R): It uses visual diagrams to show the relationships between data items.
Statement-based questions:
Direction: Questions 4-5, two statements are given: Statement 1 and Statement 2. Examine the statements and
mark the correct option:
a. Statement 1 is true, Statement 2 is true
b. Statement 1 is false, Statement 2 is false

c. Statement 1 is true, Statement 2 is false
d Statement 1 is false, Statement 2 is true
4. Statement 1: In regression, the independent variable is the one we predict.
Statement 2: The dependent variable is affected by the independent variable.

5. Statement 1: The regression line always passes through all data points.
Statement 2: The regression line tries to minimise the total error between actual and predicted values.

21 st
Century #Interdisciplinary
Skills
Regression is widely used in AI to predict continuous values, such as stock prices or weather forecasts. How do
you think regression models can impact real-life applications, like predicting sales trends or determining property
prices? How can AI improve the accuracy of these predictions over time?

21 st
Deep Thinking Century #Interdisciplinary
Skills
Data modelling is essential for structuring and interpreting complex data in AI systems. How do you think the
choice of data model whether relational, hierarchical, or dimensional affects the accuracy and efficiency of AI
predictions? Can AI systems adapt their data models to different types of problems, or is human intervention still
needed for optimal results?

Data Modelling and Simple Linear Regression 259