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

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

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

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

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

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

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

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 just right and generalizes well to unseen data.
D) When a model is too complex and captures noise in the data.

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