Linear Regression Modelling
After all of the significant factors, and interactions have been identified in the factorial design, the data can be fit to a regression model
Where Y is the response variable, xk are the independent variables, or factors, βk are the unknown parameter
coefficients, and ϵ is the error term. Determining the β coefficients will allow for us to describe the relationship
between independent variables, and the response variable (dependent variable) through an approximate
function. Since there are multiple phases that compose the 2032Nb system, a set of regressions functions will
need to be determined to appropriately describe the system. After these functions have been approximated, a
model for optimizing the system can be drawn, either through statistical or linear programming methods.
Using linear regression models to fit the factorial design data, assumes that the data fits linearly with the response variable. This assumption neglects any significant interactions that were uncovered in the factorial design, as they would be regarded as polynomial terms. However, a linear model may still be used by approximating these polynomial terms as new variables. For example if the significant terms in the regression model were Si,Nb, and Si × Nb, the regression function would be Y = β0 + β1Si + β2Nb + β3NbSi + ϵ. If we let x3 = NbSi this modifies the equation from containing two independent variables to incorporating three independent variables, and can now be considered a linear function.
The linear coefficient can be solved for using the Least Squares Method described in the next section. The regression function should then be tested for significance, and how well if fits with the original data. The easiest way to compare the fit of the approximated function is to calculate the coefficient of multiple determination , or R2 value. R2 is calculated as
The R2 value will be a fraction of how much of the model accounts for the variability in the original data. For example if R2 = 0.95, 95% of the variability of the response data is accounted for in the regression model. There are some short-comings of the R2 value as it keeps improving as more terms are added to the model. This is compensated for in the adjusted R2 value, but for the purposes of this study only the regular R2 value will be reported.
The least squares method is an effective method in solving a system of linear equations, and is used in both regression modelling, and fitting experimental data to Gibbs energy models. If a set of linear equations are described as
Where xij is the ith observation of the k total independent variables xj, yi is the dependent variable of observation i, βj is the coefficient for the jth term, and ϵ is the error between the calculated ŷi and the measured yi values . The best fits for the βj coefficients are determined by the minimization of the least squares function,
which can be expressed as
Rearranging for yi a set of n equations can be solved for providing the best estimates for βj.
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- Element, or species, that occupies a specific sublattice of a specific phase. A phase can also
be considered as a constituent of the total system.. 5, 14
- The final chemical formula of a stable or metastable phase whose sublattice(s) are
occupied by single constituents. For example M23C6 is an end member of (Cr,Ni,Fe,Nb)23C6.. 8
- The independent variable of a factorial design. 23
- main effect
- How much the change in an individual factor effects the change in the response variable of
a factorial design.. 24
- Independent repetition of a treatment in a factorial experiment. 26
- response variable
- The dependent variable of a factorial experiment, or a regression model.. 24
- A computational thermodynamics program that can calculate equilibrium phase diagrams for multicomponent systems, as well as Scheil simulations, and various thermodynamic properties (Cp, ΔHm, ΔGm etc...). 3
- A specific level of a factor in a factorial design.. 24
- the effect of the ith level of factor ‘B’
- random error component
- for all instances of ...
- in a set ...
- Overall mean effect
- Chemical potential of component or end-member i
- the effect of the ith level of factor ‘C’
- the effect of the ith level of factor ‘A’
- total Gibbs energy; G = ∑ αmα ·Gmα
- partial Gibbs energy of component i in phase α; Giα = T,P,Nj
- integral molar Gibbs energy of a phase
- constituent array of order i
- interaction parameter of compound I
- fraction of a phase
- moles of component i
- gas constant, 8.314Jmol-1K-1
- coefficient of multiple determination
- molar entropy of a phase
- Temperature (K)
- total mol fraction of component i; xi = ∑ αmα · xiα
- mole fraction of component i in phase α