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

A) The number of variables in the model.
B) The type of statistical test used.
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) When a model is too complex and captures noise in the data.
B) When a model perfectly fits the training data but fails on new 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) PCA
B) ANOVA
C) Logistic regression
D) Decision tree

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

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

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