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

A) When a model is just right and generalizes well to unseen data.
B) When a model is too complex and captures noise in the 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) Logistic regression
B) ANOVA
C) PCA
D) Decision tree

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

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

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