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

A) When a model perfectly fits the training data but fails on new data.
B) When a model is too complex and captures noise in the 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) ANOVA
B) Decision tree
C) Logistic regression
D) PCA

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

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

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

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.