Linear regression is a statistical method that describes the relationship between a target variable and one or more predictors using a straight line or plane. The coefficients are usually chosen to minimise the sum of squared deviations. The method is easy to understand and interpret: for example, it can estimate how much revenue rises when the advertising budget increases by €1,000. It reaches its limits with non-linear relationships.
Logistic regression is a statistical method that predicts the probability of one of two outcomes, such as “buys” or “does not buy”. It weights the input features and converts their sum into a value between 0 and 1 using an S-shaped function. Because the weights are easy to interpret, it is widely used in credit scoring, medicine and marketing, often as a solid baseline for more complex models.
Mean absolute error (MAE) shows how far a model’s predictions deviate from the actual values on average, regardless of direction. It is expressed in the unit of the target variable, which makes it easy to understand. Example: an MAE of 12 for a daily sales forecast means the model is off by 12 units on average. It gives less weight to outliers than squared error measures do.
Adjusted R-squared measures how well a regression model explains the variation in the data, corrected for the number of predictors used. Plain R-squared rises with every extra variable, even one that adds nothing; the adjusted version penalises unnecessary variables. This allows a fairer comparison of models of different sizes, for instance a sales forecast using three rather than ten factors.
Predictive modelling is the technical core of predictive analytics: example data with known outcomes is used to build a model that predicts a value or class for new cases. Common methods include regression, decision trees, random forests and neural networks. A model might, for instance, estimate a property price from floor area, location and year of construction. Before deployment it is tested on held-back data to detect overfitting.
