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

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

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

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

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

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

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