DSE Study Guide 2026

A choropleth map shades geographic regions by intensity of a variable, making spatial patterns in continuous data (like income or population density) immediately visible.

The ACF measures the correlation between a time series and its own lagged values at different time lags, helping identify patterns and the order of MA terms.

Statistical modeling's primary objective is to draw conclusions or make predictions about a larger population based on a sample of data. This process of generalizing from a sample to a population is known as statistical inference. While models can summarize data, their core purpose is to make informed inferences about underlying relationships or future outcomes.

MAPE involves dividing by actual values, so it is undefined at zero; it also treats positive and negative errors asymmetrically, over-penalizing forecasts that exceed actual values.

Data science has a wide range of transformative applications in healthcare. It significantly aids in drug discovery by analyzing vast biological and chemical datasets to identify potential compounds and predict their efficacy. It is also crucial for genomics, interpreting complex genetic information, and enhances medical imaging by improving diagnostic accuracy and automating analysis. These diverse applications collectively improve patient care, research, and operational efficiency.

Collinearity, or multicollinearity, occurs when predictor variables in a model are highly correlated with each other. While collinearity itself doesn't directly reduce dimensionality, addressing it often involves techniques like Principal Component Analysis (PCA) or feature selection. These methods aim to reduce the number of interdependent variables, thereby achieving dimensionality reduction and improving model stability and interpretability.