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

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

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

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

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

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

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.