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

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

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

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

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

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