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

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

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

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

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

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

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

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