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) How well the model fits the observed data.
C) The type of statistical test used.
D) The size of the dataset.

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

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

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

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

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 too simple and lacks predictive power.
D) When a model is just right and generalizes well to unseen data.

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