Unit 2

In regression analysis, the variable we are trying to predict is called the:

• A. Independent variable

• B. Dependent variable

• C. Feature variable

• D. Predictive variable

Which of the following types of data is most suitable for regression analysis?

• A. Continuous data

• B. Categorical data

• C. Binary data

• D. Ordinal data

In a linear regression model, which of the following represents the relationship between the independent and dependent variables?

• A. Correlation coefficient

• B. Regression line

• C. Residuals

• D. Outliers

Which of the following metrics is commonly used to evaluate regression models?

• A. Confusion Matrix

• B. Precision and Recall

• C. Mean Absolute Error (MAE)

• D. ROC-AUC

Which regression algorithm is most likely to be used when there is a non-linear relationship between the input features and target variable?

• A. Linear Regression

• B. Polynomial Regression

• C. Logistic Regression

• D. Ridge Regression

In multiple linear regression, what is the goal?

• A. To predict a continuous variable using multiple predictors

• B. To classify data into categories

• C. To minimize errors in binary classification

• D. To optimize the weights in a neural network

 In polynomial regression, the degree of the polynomial refers to:

• A. The number of independent variables

• B. The complexity of the model

• C. The number of observations in the dataset

• D. The number of interactions between variables

Which of the following is used to measure the goodness of fit of a regression model?

• A. Confusion matrix

• B. R-squared (R²)

• C. ROC curve

• D. Precision and Recall

What is the residual in Simple Linear Regression?

• A. The predicted value of the dependent variable

• B. The difference between the observed value and the predicted value

• C. The slope of the regression line

• D. The independent variable

What would be the effect of having outliers in the dataset when performing simple linear regression?

• A. They would have no effect on the model

• B. They may influence the slope and intercept of the regression line, leading to biased results

• C. They would improve the accuracy of the model

• D. They would increase the R-squared value