A) To examine the relationship between variables.
B) To create visual representations of data.
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) Normal distribution of residuals
B) Homoscedasticity
C) Independence of observations
D) Linearity

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 simple and lacks predictive power.
D) When a model is too complex and captures noise in the data.

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

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

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

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

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