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

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

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

A) When a model is too simple and lacks predictive power.
B) When a model is just right and generalizes well to unseen data.
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) Logistic regression
B) ANOVA
C) PCA
D) Decision tree

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

A) Cross-validation
B) Chi-square test
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 create new input variables from existing data to improve model performance.
D) To automate the entire modelling process.

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.