Dear all,
We are establishing statistical equations to predict lime requirement (LR) of tropical soils. I've found in the literature that, for prediction purpose, sometimes regression equations are used, but other times models are employed. Can anyone give us the main difference between a statistical equation and a model ? And which one is most suitable for prediction purpose ? Thanks a lot.
Semantically, "model" is a higher-level designation, and "mathematical equation" is one special form in which a model can be formulated.
A "statistical model" is essentially the definition of a random variable (RV) used to explain observed values (data). This way, the RV is a model of the data-generating process. Additionally, a statistical model may also include a mathematical equation describing (modelling!) the relationship between some features of the RV (typically the expectation of its distribution) and other (experimental) variables.
Markov models are one possible different approach of modeling the state of a system with a RV that changes through time (or simulation cycle).
I agree with Jochen Wilhelm . One or more regression equations can be part of a model (e.g., regression model, path model, factor model, or structural equation model). In practice, at least in my field (psychology), we often use the terms "regression equation" and "regression model" interchangeably. For example, we mean the same thing when we say "We used a regression equation to predict Y" versus "We used a regression model to predict Y."
The terms "statistical equation" and "model" are often used interchangeably. Both involve using mathematical relationships to describe patterns in data. The main difference lies in the scope, with "model" generally referring to a broader framework that may include multiple equations or assumptions. For predicting lime requirement, either a regression equation or a model can be suitable, depending on the complexity of the relationships among variables. The choice depends on the accuracy, interpretability, and practicality of the specific approach for your data and purpose.
- Badges
- Science topic
- Similar topics
- Biological Science
- Histology
- Sectioning