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

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

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

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

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

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

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

A) When a model is too complex and captures noise in the data.
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 simple and lacks predictive power.

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