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

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

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

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

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

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

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

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.