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

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

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

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

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

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

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

A) When a model is too simple and lacks predictive power.
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 perfectly fits the training data but fails on new data.

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