Virtual Labs

Student Performance Prediction Using Linear Regression

In the simple linear regression model Y = β0 + β1X + ε, what does the slope (β1) represent?

a: The predicted exam score Explanation

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b: The average exam score of all students Explanation

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c: The average increase in exam score for each additional hour studied Explanation

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d: The random error added to the prediction Explanation

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What does the intercept (β0) indicate in the linear regression equation?

a: The exam score when study hours are zero Explanation

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b: The variance of exam scores Explanation

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c: The strength of correlation between X and Y Explanation

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d: The error added to the model Explanation

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Which metric is used to measure the average squared error between predicted and actual exam scores?

a: Standard deviation Explanation

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b: Root Mean Squared Error (RMSE) Explanation

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c: Mean Squared Error (MSE) Explanation

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d: R-squared Explanation

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Why do we split the dataset into training (80%) and testing (20%) sets?

a: To visualize the scatter plot more clearly Explanation

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b: To check how well the model performs on unseen data Explanation

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c: To remove outliers from the dataset Explanation

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d: To increase the size of the dataset artificially Explanation

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Which assumption must hold true for applying simple linear regression?

a: There should be a linear relationship between study hours and exam score Explanation

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b: The dataset must contain more than 1000 rows Explanation

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c: Study hours must follow a normal distribution Explanation

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d: The exam scores must have no variance Explanation

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Why is Exploratory Data Analysis (EDA) performed before fitting the regression model?

a: To visually check if a linear trend exists between study hours and exam scores Explanation

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b: To automatically compute the regression coefficients β0 and β1 Explanation

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c: To convert categorical variables into numerical values Explanation

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d: To remove the linear relationship from the data Explanation

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