Use Nonlinear Regression when you cannot adequately model the relationship between a continuous response variable and one or more predictor variables with ordinary least squares …

Nonlinear regression generates an equation to describe the nonlinear relationship between a continuous response variable and one or more predictor variables, and predicts new …

Potential nonlinear shapes include concave, convex, exponential growth or decay, sigmoidal (S), and asymptotic curves. You need to specify the function that satisfies both the requirements of …

Unlike linear regression, a nonlinear regression equation can take many forms. For nonlinear equations, determining the effect that each predictor has on the response can be less intuitive …

Open the sample data, CopperExpansion.MWX. Choose Stat > Regression > Nonlinear Regression. In Response, enter Expansion. In Edit directly, copy and paste, or type the …

Stat > Regression > Nonlinear Regression > Parameters Minitab estimates parameters by using an iterative algorithm to minimize the sum of squares of the residual error (SSE). You must …

Both linear and nonlinear regression minimize the sum of squares of the residual error (SSE) to estimate the parameters. However, they use very different approaches.

You must specify the expectation function that Minitab uses to perform nonlinear regression. Your choice for the function often depends on previous knowledge about the response curve's …

Stat > Regression > Nonlinear Regression > PredictionNew observation for predictors Enter a numeric predictor value or a worksheet column for each predictor in the expectation function. If …

Nonlinear regression uses an iterative algorithm to reduce the error sums of squares (SSE). The algorithm starts by setting the parameter values to equal the values in this table. For each …