Final answer:
A simple linear regression model is not appropriate for forecasting stationary time-series data because these data have constant mean and variance over time, with no trends or seasonal effects. Alternative models suitable for stationary data include naive forecasting, averaging models, or exponential smoothing models. b. an averaging model
Step-by-step explanation:
A forecasting technique that is not appropriate for forecasting time-series data that are stationary is c. a simple linear regression model. Time-series data that are stationary exhibit a constant mean and variance over time, and therefore trends or seasonal effects are typically not present. Simple linear regression models are best suited for data that have a linear trend over time. In the case of stationary data, models like a naive forecasting model, an averaging model, or an exponential smoothing model might be more appropriate as they can handle the constant mean and autocorrelation present in stationary series.
Models serve to provide predictions and although models such as the empirical model can be based on observations, the accuracy and speed of these predictions can vary. For example, a statistical model might predict journey times or departure times with some variability. In contrast, a mathematical model can precisely determine distances that don't change such as the distance from your house to school.
When examining time series data, it's crucial to consider whether the series is stationary or not. For nonstationary time series, methods such as NHT power analyses, nonlinear models, and applications of multiple time series models are recommended. These approaches help in understanding the dynamics of trends and feedbacks, which could include both exogenous and endogenous influences.