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

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

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

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

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

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) Chi-square test
D) Principal component analysis

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

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