A) To examine the relationship between variables.
B) To create visual representations of data.
C) To calculate averages of numeric 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) Homoscedasticity
C) Independence of observations
D) Normal distribution of residuals

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

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

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

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

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

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