A) To examine the relationship between variables.
B) To calculate averages of numeric data.
C) To summarize categorical data.
D) To create visual representations of data.

A) How well the model fits the observed data.
B) The number of variables in the model.
C) The type of statistical test used.
D) The size of the dataset.

A) Independence of observations
B) Linearity
C) Homoscedasticity
D) Normal distribution of residuals

A) To fit the model exactly to the training data.
B) To create new input variables from existing data to improve model performance.
C) To automate the entire modelling process.
D) To remove all input variables except the most important one.

A) PCA
B) Logistic regression
C) Decision tree
D) ANOVA

A) To create a single composite measure from multiple variables.
B) To group similar data points together based on patterns or features.
C) To plot data points in a two-dimensional space.
D) To investigate cause-and-effect relationships.

A) Principal component analysis
B) Regression analysis
C) Cross-validation
D) Chi-square test

A) When a model is too simple and lacks predictive power.
B) When a model perfectly fits the training data but fails on new data.
C) When a model is too complex and captures noise in the data.
D) When a model is just right and generalizes well to unseen data.

A) To summarize the distribution of a dataset.
B) To assess the goodness of fit in logistic regression.
C) To evaluate the performance of a classification model.
D) To test the linearity assumption in regression models.