Regression Analysis Quiz

What is the primary goal of modeling in the context of forecasting housing prices?

• To classify housing prices into different categories

• To construct a model that predicts the median housing prices using available metrics

• To visualize the distribution of housing prices

• To cluster housing prices into different regions

Which of the following statements best describes regression analysis?

• It models the relationship between two categorical variables.

• It estimates the relationship between a target variable and one or more input features.

• It creates decision boundaries for classification problems.

• It visualizes the distribution of input features.

What does the '𝜃0' represent in a simple linear regression model?

• The slope of the regression line

• The mean squared error

• • The Y-intercept of the regression line

• The predicted value of the dependent variable

In multiple linear regression, what does the term '𝜃1𝑥1 + 𝜃2𝑥2 + … + 𝜃𝑛𝑥𝑛' represent?

• The model's error term

• The linear combination of predictors

• The intercept term

• The polynomial features of the predictors

What is the primary difference between linear regression and polynomial regression?

• Polynomial regression uses non-linear transformations of the input features.

• Linear regression includes regularization to prevent overfitting.

• Polynomial regression only works with binary classification problems.

• Linear regression can only handle one input feature.

Which metric is used to measure the average prediction error in a regression model?

• Mean Absolute Error (MAE)

• Accuracy

• Precision

• F1 Score

What does overfitting in a model refer to?

• A model that is too simple to capture underlying patterns

• A model that is too complex and fits the training data too closely

• A model that fails to predict any values correctly

• A model that has a high bias and low variance

Which technique is used to prevent overfitting by penalizing large coefficients in regression models?

• Ridge Regression

• Principal Component Analysis

• K-Nearest Neighbors

• Support Vector Machines

What is the purpose of regularization in regression models?

• To simplify the model by reducing the number of predictors

• To enhance the model's predictive accuracy on training data

• To prevent the model from fitting noise and overfitting the training data

• To increase the computational efficiency of the model

Which of the following is a common sign of underfitting in a model?

• Low training error and high testing error

• High training error and high testing error

• Low testing error and low training error

• High testing error and low training error