ACCT 331: Introduction to Applied Artificial Intelligence

These flashcards cover key concepts in applied artificial intelligence, particularly focusing on linear regression analysis and its applications.

What is linear regression?

Linear regression is the problem of characterizing the relationship between a target variable Y and independent variables x1, x2, …, xp.

What does the regression equation Y = β0 + β1X + ϵ represent?

It represents the population regression line, which is the best linear approximation to the true relationship between dependent variable Y and independent variable X.

What are residuals in regression analysis?

Residuals are the differences between the observed values and the predicted values from the model.

What does R² measure in a regression model?

R² measures the proportion of variability in the dependent variable that can be explained by the independent variables.

What is the difference between MAE and MSE?

MAE is the average of absolute errors, while MSE is the average of squared errors, with MSE penalizing larger errors more heavily.

What is polynomial regression?

Polynomial regression extends linear regression to capture non-linear relationships by adding powers of predictors.

What are the assumptions of linear regression?

Linearity, Independence, Normality of residuals, No multicollinearity.

What is meant by 'overfitting' in regression analysis?

Overfitting refers to a model that is too complex and captures noise in the training data rather than the underlying trend.

What is the role of the intercept in a regression model?

The intercept represents the expected value of the dependent variable Y when all independent variables are zero.

What is a multiple linear regression model?

A multiple linear regression model predicts a single outcome (Y) using multiple independent variables (x1, x2, …, xn).