A) To examine the relationship between variables. B) To summarize categorical data. C) To create visual representations of data. D) To calculate averages of numeric data.
A) The number of variables in the model. B) The type of statistical test used. C) The size of the dataset. D) How well the model fits the observed data.
A) Normal distribution of residuals B) Linearity C) Homoscedasticity D) Independence of observations
A) Principal component analysis B) Chi-square test C) Regression analysis D) Cross-validation
A) To plot data points in a two-dimensional space. B) To create a single composite measure from multiple variables. C) To investigate cause-and-effect relationships. D) To group similar data points together based on patterns or features.
A) To summarize the distribution of a dataset. B) To evaluate the performance of a classification model. C) To test the linearity assumption in regression models. D) To assess the goodness of fit in logistic regression.
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 fit the model exactly to the training data. D) To automate the entire modelling process.
A) Logistic regression B) ANOVA C) PCA D) Decision tree
A) When a model is too complex and captures noise in the data. B) When a model is too simple and lacks predictive power. C) When a model is just right and generalizes well to unseen data. D) When a model perfectly fits the training data but fails on new data. |