A forecast that projects future values in a straight line is utilizing which kind of model?

A forecast that projects future values in a straight line employs a linear regression model. This type of model establishes a relationship between the dependent variable (the value being forecasted) and one or more independent variables using a straight line. Linear regression assumes that there is a constant relationship between the variables, allowing it to predict future outcomes based on the trend observed in the data.

In linear regression, the equation of the model typically takes the form (y = mx + b), where (y) is the predicted value, (m) is the slope of the line (indicating the rate of change), (x) is the independent variable, and (b) is the y-intercept. This simplicity is effective for forecasting in many scenarios where trends are linear over time.

Understanding linear regression as a method for projecting a straight line helps distinguish it from nonlinear models, which involve complex relationships that cannot be represented as a straight line; exponential smoothing, which focuses on averaging past data with decreasing weights for older values; and time series models, which might incorporate trends and seasonality but do not inherently assume a straight-line projection.