Virtual Labs

What is the main objective of Linear Regression?

a: Classify data into categories Explanation

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b: Predict continuous numerical values Explanation

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c: Group similar data points Explanation

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d: Reduce dimensionality of data Explanation

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In the Linear Regression equation Y = β₀ + β₁X + ε, the term β₁ represents:

a: Intercept of the regression line Explanation

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b: Slope of the regression line Explanation

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c: Error term Explanation

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d: Target variable Explanation

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Which variable is predicted in Linear Regression?

a: Independent variable Explanation

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b: Dependent variable Explanation

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c: Feature variable Explanation

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d: Input variable Explanation

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Which method is commonly used to estimate coefficients in Linear Regression?

a: Least Squares Method Explanation

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b: K-Means Algorithm Explanation

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c: Decision Tree Method Explanation

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d: Random Forest Method Explanation

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Which plot is most suitable for visualizing the relationship between two variables in Simple Linear Regression?

a: Scatter Plot Explanation

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b: Pie Chart Explanation

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c: Histogram Explanation

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d: Bar Chart Explanation

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Overfitting in Linear Regression is characterized by:

a: High Train Error, High Test Error Explanation

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b: Low Train Error, High Test Error Explanation

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c: High Train Error, Low Test Error Explanation

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d: Low Train Error, Low Test Error Explanation

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What does a high value of RMSE indicate?

a: The model predictions are very accurate Explanation

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b: The model has large prediction errors Explanation

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c: The dataset has more samples Explanation

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d: The model is overfitting Explanation

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If the residual plot shows a clear curved pattern, it indicates:

a: The model fits the data perfectly Explanation

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b: Violation of the linearity assumption Explanation

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c: Presence of multicollinearity Explanation

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d: Data normalization issue Explanation

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Multicollinearity in Multiple Linear Regression occurs when:

a: Independent variables are highly correlated with each other Explanation

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b: Dependent variables are correlated Explanation

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c: There are missing values in the dataset Explanation

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d: The dataset contains categorical features Explanation

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Which regularization technique can shrink regression coefficients exactly to zero?

a: Ridge Regression Explanation

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b: Lasso Regression Explanation

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c: Polynomial Regression Explanation

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d: Logistic Regression Explanation

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