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

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

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

A) When a model is too simple and lacks predictive power.
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 just right and generalizes well to unseen data.

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

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

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

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

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