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

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

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

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

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

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

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

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 automate the entire modelling process.
D) To remove all input variables except the most important one.

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