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

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

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

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

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

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

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

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