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

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

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

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

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

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 is just right and generalizes well to unseen 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 perfectly fits the training data but fails on new data.

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