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

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

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

A) To automate the entire modelling process.
B) To fit the model exactly to the training data.
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) Decision tree
B) Logistic regression
C) ANOVA
D) PCA

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

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

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

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