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 number of variables in the model.
C) How well the model fits the observed data.
D) The size of the dataset.

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

A) When a model perfectly fits the training data but fails on new data.
B) When a model is too simple and lacks predictive power.
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) PCA
B) Decision tree
C) ANOVA
D) Logistic regression

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

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

A) To fit the model exactly to the training data.
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 automate the entire modelling process.

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