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Variable - dependent, uncontrolled, "Y", output variable, outcome variable. Items and consumer will probably purchase.
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However, in nonlinear regression, the correct null hypothesis value for each parameter depends on the expectation function and the parameter's place in it.įor some data sets, expectation functions, and confidence levels, it is possible that one or both confidence bounds may not exist. For linear regression, the null hypothesis value for every parameter is zero, for no effect, and the p-value is based on this value.
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Minitab cannot calculate p-values for parameters in nonlinear regression. The parameter is statistically significant if the range excludes the null hypothesis value. If you need to determine whether a parameter estimate is statistically significant, use the confidence intervals for the parameters. If your nonlinear model contains only one predictor, assess the fitted line plot to see the relationship between the predictor and response. The correct interpretation for each parameter depends on the expectation function and the parameter's place in it. Unlike the parameter estimates in linear models, there is no consistent interpretation for the parameter estimates in nonlinear models. Unlike linear regression, a nonlinear regression equation can take many forms.įor nonlinear equations, determining the effect that each predictor has on the response can be less intuitive than it is for linear equations. Enter the value of each predictor into the equation to calculate the mean response value.
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The regression equation is an algebraic representation of the regression line. Use the regression equation to describe the relationship between the response and the terms in the model.