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

A) When a model is just right and generalizes well to unseen data.
B) When a model perfectly fits the training data but fails on new 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) ANOVA
C) Decision tree
D) PCA

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

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

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 fit the model exactly to the training data.
D) To automate the entire modelling process.

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.