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

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

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

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

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

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

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

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

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