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What is the statistical model for factorial design under randomized complete block design?
reference for factorial completly randomised design
What is a quantitative model?
The official definition for a quantitative model is " Collection of mathematical and statistical methods used in the solution of managerial and decision-making problems, also called operations research (OR) and management science."
What is CASTHY?
An optical and statistical model is what CASTHY is.Specifically, the model can be found among the Nuclear Energy Agency's data bank of computer program services. The optical model offers calculations of neutron cross-sections in terms of total, shape elastic scattering and compound nucleus formation. The statistical model provides capture, compound elastic, and inelastic cross-sections. The model also supplies calculations for capture gamma-ray spectra and cross-sections of competing processes.Whatever the calculation, the computer language is Fortran-77 and -90.
Consequences of autocorrelation?
The answer will depend on the level of statistical knowledge that you have and, unfortunately, we do not know that. The regression model is based on the assumption that the residuals [or errors] are independent and this is not true if autocorrelation is present. A simple solution is to use moving averages (MA). Other models, such as the autoregressive model (AR) or autoregressive integrated moving average model (ARIMA) can be used. Statistical software packages will include tests for the existence of autocorrelation and also applying one or more of these models to the data.
What is a response variable?
In a statistical model, you have two kinds of variable. Response variables are the "outputs" of your model. Explanatory variables, on the other hand, are the "inputs" of your model. Response variables are dependent on the explanatory variables. Explanatory variable are independent of the response variables.Imagine you were trying to formulate a statistical model of your car's fuel economy. The "output" of your model is miles per gallon (or kilometres per litre). That's your response variable. "Inputs" into your model might be (for example) engine capacity, number of cylinders, tyre pressure, etc. These are your explanatory variables. That is, fuel economy may be, or is, (to be determined by the modeling) dependent on engine capacity and/or number of cylinders and/or tyre pressure, etc.after the treatment
