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

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 perfectly fits the training data but fails on new data.
D) When a model is too complex and captures noise in the data.

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

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

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

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

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