Data Modelling Exam help needed on 14th jan 9-11am.
Example of topics included are
Maximum Likelihood Estimation (MLE):
Questions on deriving the likelihood function, negative log-likelihood function, and MLE for an exponential distribution.
Random Variables:
Problems involving Poisson distributions, expectations, variances, and probabilities.
Confidence Intervals and p-values:
Estimation and hypothesis testing with applications to real-world data, including cholesterol level analysis.
Regression:
Linear regression principles, handling categorical predictors, model interpretation
Probability and odds calculations for breast cancer prediction based on mammographic density.
Logistic Regression:
Model interpretation, probability calculations, predictions, and handling non-linear relationships.
Bias and Variance:
Derivations showing that sample mean is unbiased, its variance, and consistency.
Machine Learning:
Interpretation of decision trees and comparison with regression models, as well as k-nearest neighbors classification.